boston_airbnb_analysis.ipynb

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   "source": [
    "# <center> Boston Airbnb Analysis\n",
    "\n",
    "<img src=\"https://cdn-images-1.medium.com/max/2400/1*BfcMRSGnD5pMG61KfPqjVA.png\" style=\"width:900px;\"/>\n",
    "\n",
    "Original photo by [Anthony Delanoix](https://unsplash.com/@anthonydelanoix) on [Unsplash](https://unsplash.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "cd = os.getcwd()\n",
    "import re\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import plotly.plotly as py #for creating interactive data visualizations\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import plotly.tools as tls\n",
    "py.sign_in('salitr', '0Vm0IzVDJl70ydZG9ZjW') #API key has been removed for security\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot #to work with data visualization offline\n",
    "init_notebook_mode(connected=True)\n",
    "import cufflinks as cf #connects Plotly with pandas to produce the interactive data visualizations\n",
    "cf.go_offline()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn import metrics\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "import gmaps\n",
    "gmaps.configure(api_key='AIzaSyDVKBGsT0nQS_2V7KwS8DfSko2CVIQWjQU') #API key has been removed for security\n",
    "from ipywidgets.embed import embed_minimal_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "# <center>STAGE ONE</center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "## 1. Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "### 1.1 Business Questions\n",
    "\n",
    "<font color=blue> \n",
    "    Q1. How prices for all Boston's Airbnb fluctuate throughout the year 2019?\n",
    "\n",
    "<font color=blue> \n",
    "    Q2. What are the peak and off-peak times during the year for Airbnb rental prices in Boston?\n",
    "\n",
    "<font color=blue> \n",
    "    Q3. Who are the hosts with the most number of Airbnb listings?\n",
    "\n",
    "<font color=blue> \n",
    "    Q4. Which neighborhoods have the most number of listing in Boston?\n",
    "\n",
    "<font color=blue> \n",
    "    Q5. Which are the most expensive neighborhoods in Boston? \n",
    "    \n",
    "<font color=blue> \n",
    "    Q6. Which are the popular neighborhoods based on the average number of reviews?\n",
    "\n",
    "<font color=blue> \n",
    "    Q7. Which type of room has the majority of listings in Boston Airbnb?\n",
    "\n",
    "<font color=blue> \n",
    "    Q8. What are the features that influence the price in Boston Airbnb? Moreover, can we predict the rental price of new listings based on a predictive model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "## 2. Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the three datasets\n",
    "listings = pd.read_csv(cd+'/listings.csv')\n",
    "reviews = pd.read_csv(cd+'/reviews.csv')\n",
    "calendar = pd.read_csv(cd+'/calendar.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "### 2.1 listings data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6155, 106)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>7825327</td>\n",
       "      <td>https://www.airbnb.com/rooms/7825327</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Historical South End Brownstone T-4</td>\n",
       "      <td>Flash Sale For this Month Only Stays Up To 40%...</td>\n",
       "      <td>*We offer you the most competitive rates.  Bef...</td>\n",
       "      <td>Flash Sale For this Month Only Stays Up To 40%...</td>\n",
       "      <td>none</td>\n",
       "      <td>One of the trendiest neighborhoods in Boston, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>5048406</td>\n",
       "      <td>https://www.airbnb.com/rooms/5048406</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>3rd floor room in Victorian House</td>\n",
       "      <td>Huge 8 rooms, 5 bdrms.  house. Kitchen, living...</td>\n",
       "      <td>We offer full kitchen access: dishes, oven, co...</td>\n",
       "      <td>Huge 8 rooms, 5 bdrms.  house. Kitchen, living...</td>\n",
       "      <td>none</td>\n",
       "      <td>Dorchester is the largest and oldest residenti...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5978</th>\n",
       "      <td>31341983</td>\n",
       "      <td>https://www.airbnb.com/rooms/31341983</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Boston市区Boston College附近地铁沿线优质房源</td>\n",
       "      <td>交通极其便利,出门1mins到地铁C线,4mins到地铁D线,5mins到地铁B线,步行15...</td>\n",
       "      <td>可以使用卫生间厨房客厅等公用空间</td>\n",
       "      <td>交通极其便利,出门1mins到地铁C线,4mins到地铁D线,5mins到地铁B线,步行15...</td>\n",
       "      <td>none</td>\n",
       "      <td>靠近Boston College和Boston University</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>23090526</td>\n",
       "      <td>https://www.airbnb.com/rooms/23090526</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Great 2 bedrooms near Airport East Boston</td>\n",
       "      <td>This two cozy and comfortable bedroom apartmen...</td>\n",
       "      <td>We have everything in this place, and the gues...</td>\n",
       "      <td>This two cozy and comfortable bedroom apartmen...</td>\n",
       "      <td>none</td>\n",
       "      <td>A very nice, quiet, and safe neighborhood. The...</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1494726</td>\n",
       "      <td>https://www.airbnb.com/rooms/1494726</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Cozy in Rozzie!</td>\n",
       "      <td>Spacious &amp; quiet room w. private bath, for 1+ ...</td>\n",
       "      <td>Spacious room with private bathroom in really ...</td>\n",
       "      <td>Spacious &amp; quiet room w. private bath, for 1+ ...</td>\n",
       "      <td>none</td>\n",
       "      <td>If you like quaint villages, come to Rozzie Sq...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>25416313</td>\n",
       "      <td>https://www.airbnb.com/rooms/25416313</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Newly renovated, Best Location CENTER of Boston</td>\n",
       "      <td>1 bedroom 1 and half bathrooms available in a ...</td>\n",
       "      <td>Access to the Red Line Shawmut train station t...</td>\n",
       "      <td>1 bedroom 1 and half bathrooms available in a ...</td>\n",
       "      <td>none</td>\n",
       "      <td>I've lived here for over 15 years, it is consi...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5927267</td>\n",
       "      <td>https://www.airbnb.com/rooms/5927267</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Large room, wood floor, 2 windows</td>\n",
       "      <td>It is a great location and room is very nice t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>It is a great location and room is very nice t...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4812</th>\n",
       "      <td>27125054</td>\n",
       "      <td>https://www.airbnb.com/rooms/27125054</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Cute &amp; Spacious Home in the Heart of Local Boston</td>\n",
       "      <td>This is an entire one-floor single home with a...</td>\n",
       "      <td>This single ADA family home features lots of o...</td>\n",
       "      <td>This is an entire one-floor single home with a...</td>\n",
       "      <td>none</td>\n",
       "      <td>Dorchester is Boston's largest neighborhood wi...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>13652374</td>\n",
       "      <td>https://www.airbnb.com/rooms/13652374</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Private Room near Forest Hills T Stop</td>\n",
       "      <td>Lugares de interés: Arnold Arboretum Walter St...</td>\n",
       "      <td>Private room in the beautiful neighborhood of ...</td>\n",
       "      <td>Lugares de interés: Arnold Arboretum Walter St...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>21076367</td>\n",
       "      <td>https://www.airbnb.com/rooms/21076367</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>2/9/19</td>\n",
       "      <td>Charming 1 Bedroom in Beacon Hill (Downtown BOS)</td>\n",
       "      <td>A one bedroom apartment at the heart of Boston...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A one bedroom apartment at the heart of Boston...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                            listing_url     scrape_id  \\\n",
       "963    7825327   https://www.airbnb.com/rooms/7825327  2.019020e+13   \n",
       "602    5048406   https://www.airbnb.com/rooms/5048406  2.019020e+13   \n",
       "5978  31341983  https://www.airbnb.com/rooms/31341983  2.019020e+13   \n",
       "3930  23090526  https://www.airbnb.com/rooms/23090526  2.019020e+13   \n",
       "227    1494726   https://www.airbnb.com/rooms/1494726  2.019020e+13   \n",
       "4538  25416313  https://www.airbnb.com/rooms/25416313  2.019020e+13   \n",
       "696    5927267   https://www.airbnb.com/rooms/5927267  2.019020e+13   \n",
       "4812  27125054  https://www.airbnb.com/rooms/27125054  2.019020e+13   \n",
       "1696  13652374  https://www.airbnb.com/rooms/13652374  2.019020e+13   \n",
       "3258  21076367  https://www.airbnb.com/rooms/21076367  2.019020e+13   \n",
       "\n",
       "     last_scraped                                               name  \\\n",
       "963        2/9/19                Historical South End Brownstone T-4   \n",
       "602        2/9/19                  3rd floor room in Victorian House   \n",
       "5978       2/9/19                   Boston市区Boston College附近地铁沿线优质房源   \n",
       "3930       2/9/19          Great 2 bedrooms near Airport East Boston   \n",
       "227        2/9/19                                    Cozy in Rozzie!   \n",
       "4538       2/9/19    Newly renovated, Best Location CENTER of Boston   \n",
       "696        2/9/19                  Large room, wood floor, 2 windows   \n",
       "4812       2/9/19  Cute & Spacious Home in the Heart of Local Boston   \n",
       "1696       2/9/19              Private Room near Forest Hills T Stop   \n",
       "3258       2/9/19   Charming 1 Bedroom in Beacon Hill (Downtown BOS)   \n",
       "\n",
       "                                                summary  \\\n",
       "963   Flash Sale For this Month Only Stays Up To 40%...   \n",
       "602   Huge 8 rooms, 5 bdrms.  house. Kitchen, living...   \n",
       "5978  交通极其便利,出门1mins到地铁C线,4mins到地铁D线,5mins到地铁B线,步行15...   \n",
       "3930  This two cozy and comfortable bedroom apartmen...   \n",
       "227   Spacious & quiet room w. private bath, for 1+ ...   \n",
       "4538  1 bedroom 1 and half bathrooms available in a ...   \n",
       "696   It is a great location and room is very nice t...   \n",
       "4812  This is an entire one-floor single home with a...   \n",
       "1696  Lugares de interés: Arnold Arboretum Walter St...   \n",
       "3258  A one bedroom apartment at the heart of Boston...   \n",
       "\n",
       "                                                  space  \\\n",
       "963   *We offer you the most competitive rates.  Bef...   \n",
       "602   We offer full kitchen access: dishes, oven, co...   \n",
       "5978                                   可以使用卫生间厨房客厅等公用空间   \n",
       "3930  We have everything in this place, and the gues...   \n",
       "227   Spacious room with private bathroom in really ...   \n",
       "4538  Access to the Red Line Shawmut train station t...   \n",
       "696                                                 NaN   \n",
       "4812  This single ADA family home features lots of o...   \n",
       "1696  Private room in the beautiful neighborhood of ...   \n",
       "3258                                                NaN   \n",
       "\n",
       "                                            description experiences_offered  \\\n",
       "963   Flash Sale For this Month Only Stays Up To 40%...                none   \n",
       "602   Huge 8 rooms, 5 bdrms.  house. Kitchen, living...                none   \n",
       "5978  交通极其便利,出门1mins到地铁C线,4mins到地铁D线,5mins到地铁B线,步行15...                none   \n",
       "3930  This two cozy and comfortable bedroom apartmen...                none   \n",
       "227   Spacious & quiet room w. private bath, for 1+ ...                none   \n",
       "4538  1 bedroom 1 and half bathrooms available in a ...                none   \n",
       "696   It is a great location and room is very nice t...                none   \n",
       "4812  This is an entire one-floor single home with a...                none   \n",
       "1696  Lugares de interés: Arnold Arboretum Walter St...                none   \n",
       "3258  A one bedroom apartment at the heart of Boston...                none   \n",
       "\n",
       "                                  neighborhood_overview  ... instant_bookable  \\\n",
       "963   One of the trendiest neighborhoods in Boston, ...  ...                t   \n",
       "602   Dorchester is the largest and oldest residenti...  ...                f   \n",
       "5978                 靠近Boston College和Boston University  ...                f   \n",
       "3930  A very nice, quiet, and safe neighborhood. The...  ...                t   \n",
       "227   If you like quaint villages, come to Rozzie Sq...  ...                f   \n",
       "4538  I've lived here for over 15 years, it is consi...  ...                f   \n",
       "696                                                 NaN  ...                t   \n",
       "4812  Dorchester is Boston's largest neighborhood wi...  ...                f   \n",
       "1696                                                NaN  ...                f   \n",
       "3258                                                NaN  ...                f   \n",
       "\n",
       "     is_business_travel_ready          cancellation_policy  \\\n",
       "963                         f  strict_14_with_grace_period   \n",
       "602                         f  strict_14_with_grace_period   \n",
       "5978                        f  strict_14_with_grace_period   \n",
       "3930                        f                     moderate   \n",
       "227                         f  strict_14_with_grace_period   \n",
       "4538                        f                     moderate   \n",
       "696                         f                     moderate   \n",
       "4812                        f                     moderate   \n",
       "1696                        f                     flexible   \n",
       "3258                        f  strict_14_with_grace_period   \n",
       "\n",
       "     require_guest_profile_picture require_guest_phone_verification  \\\n",
       "963                              f                                f   \n",
       "602                              f                                f   \n",
       "5978                             f                                f   \n",
       "3930                             f                                f   \n",
       "227                              f                                f   \n",
       "4538                             f                                f   \n",
       "696                              f                                f   \n",
       "4812                             f                                f   \n",
       "1696                             f                                f   \n",
       "3258                             f                                f   \n",
       "\n",
       "      calculated_host_listings_count  \\\n",
       "963                               12   \n",
       "602                                2   \n",
       "5978                               1   \n",
       "3930                               2   \n",
       "227                                1   \n",
       "4538                               7   \n",
       "696                                4   \n",
       "4812                               1   \n",
       "1696                               1   \n",
       "3258                               1   \n",
       "\n",
       "      calculated_host_listings_count_entire_homes  \\\n",
       "963                                            12   \n",
       "602                                             0   \n",
       "5978                                            0   \n",
       "3930                                            2   \n",
       "227                                             0   \n",
       "4538                                            1   \n",
       "696                                             0   \n",
       "4812                                            1   \n",
       "1696                                            0   \n",
       "3258                                            1   \n",
       "\n",
       "     calculated_host_listings_count_private_rooms  \\\n",
       "963                                             0   \n",
       "602                                             2   \n",
       "5978                                            1   \n",
       "3930                                            0   \n",
       "227                                             1   \n",
       "4538                                            6   \n",
       "696                                             4   \n",
       "4812                                            0   \n",
       "1696                                            1   \n",
       "3258                                            0   \n",
       "\n",
       "      calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "963                                             0               0.52  \n",
       "602                                             0               0.57  \n",
       "5978                                            0                NaN  \n",
       "3930                                            0               0.91  \n",
       "227                                             0               0.26  \n",
       "4538                                            0               0.40  \n",
       "696                                             0               2.05  \n",
       "4812                                            0                NaN  \n",
       "1696                                            0                NaN  \n",
       "3258                                            0               0.12  \n",
       "\n",
       "[10 rows x 106 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the dataset\n",
    "print(listings.shape)\n",
    "listings.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.155000e+03</td>\n",
       "      <td>6.155000e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.155000e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6153.000000</td>\n",
       "      <td>6153.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4854.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6155.000000</td>\n",
       "      <td>6155.000000</td>\n",
       "      <td>6155.000000</td>\n",
       "      <td>6155.000000</td>\n",
       "      <td>4911.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.879144e+07</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.998043e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.358524</td>\n",
       "      <td>152.358524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.681706</td>\n",
       "      <td>9.545886</td>\n",
       "      <td>9.300474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.479285</td>\n",
       "      <td>33.577417</td>\n",
       "      <td>1.840780</td>\n",
       "      <td>0.061089</td>\n",
       "      <td>1.976685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.802758e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.481161e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372.054375</td>\n",
       "      <td>372.054375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.807092</td>\n",
       "      <td>0.777950</td>\n",
       "      <td>0.891518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.147833</td>\n",
       "      <td>73.626063</td>\n",
       "      <td>4.079204</td>\n",
       "      <td>0.540380</td>\n",
       "      <td>2.100526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.781000e+03</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.804000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.278680e+07</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.180787e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.043183e+07</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.028359e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.601302e+07</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.545940e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.224338e+07</td>\n",
       "      <td>2.019020e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.418780e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     scrape_id  thumbnail_url  medium_url  xl_picture_url  \\\n",
       "count  6.155000e+03  6.155000e+03            0.0         0.0             0.0   \n",
       "mean   1.879144e+07  2.019020e+13            NaN         NaN             NaN   \n",
       "std    8.802758e+06  0.000000e+00            NaN         NaN             NaN   \n",
       "min    3.781000e+03  2.019020e+13            NaN         NaN             NaN   \n",
       "25%    1.278680e+07  2.019020e+13            NaN         NaN             NaN   \n",
       "50%    2.043183e+07  2.019020e+13            NaN         NaN             NaN   \n",
       "75%    2.601302e+07  2.019020e+13            NaN         NaN             NaN   \n",
       "max    3.224338e+07  2.019020e+13            NaN         NaN             NaN   \n",
       "\n",
       "            host_id  host_acceptance_rate  host_listings_count  \\\n",
       "count  6.155000e+03                   0.0          6153.000000   \n",
       "mean   5.998043e+07                   NaN           152.358524   \n",
       "std    6.481161e+07                   NaN           372.054375   \n",
       "min    4.804000e+03                   NaN             0.000000   \n",
       "25%    1.180787e+07                   NaN             1.000000   \n",
       "50%    3.028359e+07                   NaN             4.000000   \n",
       "75%    9.545940e+07                   NaN            38.000000   \n",
       "max    2.418780e+08                   NaN          1480.000000   \n",
       "\n",
       "       host_total_listings_count  neighbourhood_group_cleansed  ...  \\\n",
       "count                6153.000000                           0.0  ...   \n",
       "mean                  152.358524                           NaN  ...   \n",
       "std                   372.054375                           NaN  ...   \n",
       "min                     0.000000                           NaN  ...   \n",
       "25%                     1.000000                           NaN  ...   \n",
       "50%                     4.000000                           NaN  ...   \n",
       "75%                    38.000000                           NaN  ...   \n",
       "max                  1480.000000                           NaN  ...   \n",
       "\n",
       "       review_scores_communication  review_scores_location  \\\n",
       "count                  4854.000000             4849.000000   \n",
       "mean                      9.681706                9.545886   \n",
       "std                       0.807092                0.777950   \n",
       "min                       2.000000                2.000000   \n",
       "25%                      10.000000                9.000000   \n",
       "50%                      10.000000               10.000000   \n",
       "75%                      10.000000               10.000000   \n",
       "max                      10.000000               10.000000   \n",
       "\n",
       "       review_scores_value  license  jurisdiction_names  \\\n",
       "count          4849.000000      0.0                 0.0   \n",
       "mean              9.300474      NaN                 NaN   \n",
       "std               0.891518      NaN                 NaN   \n",
       "min               2.000000      NaN                 NaN   \n",
       "25%               9.000000      NaN                 NaN   \n",
       "50%               9.000000      NaN                 NaN   \n",
       "75%              10.000000      NaN                 NaN   \n",
       "max              10.000000      NaN                 NaN   \n",
       "\n",
       "       calculated_host_listings_count  \\\n",
       "count                     6155.000000   \n",
       "mean                        35.479285   \n",
       "std                         73.147833   \n",
       "min                          1.000000   \n",
       "25%                          1.000000   \n",
       "50%                          3.000000   \n",
       "75%                         30.000000   \n",
       "max                        306.000000   \n",
       "\n",
       "       calculated_host_listings_count_entire_homes  \\\n",
       "count                                  6155.000000   \n",
       "mean                                     33.577417   \n",
       "std                                      73.626063   \n",
       "min                                       0.000000   \n",
       "25%                                       0.000000   \n",
       "50%                                       1.000000   \n",
       "75%                                      21.000000   \n",
       "max                                     306.000000   \n",
       "\n",
       "       calculated_host_listings_count_private_rooms  \\\n",
       "count                                   6155.000000   \n",
       "mean                                       1.840780   \n",
       "std                                        4.079204   \n",
       "min                                        0.000000   \n",
       "25%                                        0.000000   \n",
       "50%                                        0.000000   \n",
       "75%                                        1.000000   \n",
       "max                                       30.000000   \n",
       "\n",
       "       calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "count                                  6155.000000        4911.000000  \n",
       "mean                                      0.061089           1.976685  \n",
       "std                                       0.540380           2.100526  \n",
       "min                                       0.000000           0.010000  \n",
       "25%                                       0.000000           0.380000  \n",
       "50%                                       0.000000           1.190000  \n",
       "75%                                       0.000000           3.000000  \n",
       "max                                       8.000000          13.710000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of each feature\n",
    "listings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'listing_url',\n",
       " 'scrape_id',\n",
       " 'last_scraped',\n",
       " 'name',\n",
       " 'summary',\n",
       " 'space',\n",
       " 'description',\n",
       " 'experiences_offered',\n",
       " 'neighborhood_overview',\n",
       " 'notes',\n",
       " 'transit',\n",
       " 'access',\n",
       " 'interaction',\n",
       " 'house_rules',\n",
       " 'thumbnail_url',\n",
       " 'medium_url',\n",
       " 'picture_url',\n",
       " 'xl_picture_url',\n",
       " 'host_id',\n",
       " 'host_url',\n",
       " 'host_name',\n",
       " 'host_since',\n",
       " 'host_location',\n",
       " 'host_about',\n",
       " 'host_response_time',\n",
       " 'host_response_rate',\n",
       " 'host_acceptance_rate',\n",
       " 'host_is_superhost',\n",
       " 'host_thumbnail_url',\n",
       " 'host_picture_url',\n",
       " 'host_neighbourhood',\n",
       " 'host_listings_count',\n",
       " 'host_total_listings_count',\n",
       " 'host_verifications',\n",
       " 'host_has_profile_pic',\n",
       " 'host_identity_verified',\n",
       " 'street',\n",
       " 'neighbourhood',\n",
       " 'neighbourhood_cleansed',\n",
       " 'neighbourhood_group_cleansed',\n",
       " 'city',\n",
       " 'state',\n",
       " 'zipcode',\n",
       " 'market',\n",
       " 'smart_location',\n",
       " 'country_code',\n",
       " 'country',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'is_location_exact',\n",
       " 'property_type',\n",
       " 'room_type',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'bed_type',\n",
       " 'amenities',\n",
       " 'square_feet',\n",
       " 'price',\n",
       " 'weekly_price',\n",
       " 'monthly_price',\n",
       " 'security_deposit',\n",
       " 'cleaning_fee',\n",
       " 'guests_included',\n",
       " 'extra_people',\n",
       " 'minimum_nights',\n",
       " 'maximum_nights',\n",
       " 'minimum_minimum_nights',\n",
       " 'maximum_minimum_nights',\n",
       " 'minimum_maximum_nights',\n",
       " 'maximum_maximum_nights',\n",
       " 'minimum_nights_avg_ntm',\n",
       " 'maximum_nights_avg_ntm',\n",
       " 'calendar_updated',\n",
       " 'has_availability',\n",
       " 'availability_30',\n",
       " 'availability_60',\n",
       " 'availability_90',\n",
       " 'availability_365',\n",
       " 'calendar_last_scraped',\n",
       " 'number_of_reviews',\n",
       " 'number_of_reviews_ltm',\n",
       " 'first_review',\n",
       " 'last_review',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'review_scores_cleanliness',\n",
       " 'review_scores_checkin',\n",
       " 'review_scores_communication',\n",
       " 'review_scores_location',\n",
       " 'review_scores_value',\n",
       " 'requires_license',\n",
       " 'license',\n",
       " 'jurisdiction_names',\n",
       " 'instant_bookable',\n",
       " 'is_business_travel_ready',\n",
       " 'cancellation_policy',\n",
       " 'require_guest_profile_picture',\n",
       " 'require_guest_phone_verification',\n",
       " 'calculated_host_listings_count',\n",
       " 'calculated_host_listings_count_entire_homes',\n",
       " 'calculated_host_listings_count_private_rooms',\n",
       " 'calculated_host_listings_count_shared_rooms',\n",
       " 'reviews_per_month']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#listing all the features\n",
    "list(listings.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "xl_picture_url                                  6155\n",
       "neighbourhood_group_cleansed                    6155\n",
       "host_acceptance_rate                            6155\n",
       "jurisdiction_names                              6155\n",
       "license                                         6155\n",
       "medium_url                                      6155\n",
       "thumbnail_url                                   6155\n",
       "square_feet                                     6057\n",
       "weekly_price                                    5628\n",
       "monthly_price                                   5626\n",
       "access                                          2463\n",
       "notes                                           2445\n",
       "interaction                                     2064\n",
       "host_about                                      2025\n",
       "house_rules                                     1974\n",
       "neighborhood_overview                           1952\n",
       "security_deposit                                1875\n",
       "transit                                         1790\n",
       "space                                           1401\n",
       "review_scores_checkin                           1306\n",
       "review_scores_location                          1306\n",
       "review_scores_value                             1306\n",
       "review_scores_accuracy                          1304\n",
       "review_scores_cleanliness                       1302\n",
       "review_scores_communication                     1301\n",
       "review_scores_rating                            1299\n",
       "reviews_per_month                               1244\n",
       "last_review                                     1244\n",
       "first_review                                    1244\n",
       "host_response_time                              1238\n",
       "                                                ... \n",
       "instant_bookable                                   0\n",
       "is_business_travel_ready                           0\n",
       "cancellation_policy                                0\n",
       "require_guest_profile_picture                      0\n",
       "require_guest_phone_verification                   0\n",
       "calculated_host_listings_count                     0\n",
       "calculated_host_listings_count_entire_homes        0\n",
       "calculated_host_listings_count_private_rooms       0\n",
       "calendar_updated                                   0\n",
       "minimum_nights_avg_ntm                             0\n",
       "country_code                                       0\n",
       "maximum_maximum_nights                             0\n",
       "country                                            0\n",
       "latitude                                           0\n",
       "longitude                                          0\n",
       "is_location_exact                                  0\n",
       "property_type                                      0\n",
       "calculated_host_listings_count_shared_rooms        0\n",
       "accommodates                                       0\n",
       "bed_type                                           0\n",
       "amenities                                          0\n",
       "price                                              0\n",
       "guests_included                                    0\n",
       "extra_people                                       0\n",
       "minimum_nights                                     0\n",
       "maximum_nights                                     0\n",
       "minimum_minimum_nights                             0\n",
       "maximum_minimum_nights                             0\n",
       "minimum_maximum_nights                             0\n",
       "id                                                 0\n",
       "Length: 106, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the nulls for each feature\n",
    "print(listings.isnull().sum().any())\n",
    "listings.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "As the result of the above cell reports, many of the listings dataset's features have missing values. In fact, some of them has no entry at all. An issue that has to be addressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of duplicates\n",
    "listings.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group_cleansed                      0\n",
       "xl_picture_url                                    0\n",
       "medium_url                                        0\n",
       "thumbnail_url                                     0\n",
       "license                                           0\n",
       "jurisdiction_names                                0\n",
       "host_acceptance_rate                              0\n",
       "state                                             1\n",
       "has_availability                                  1\n",
       "country_code                                      1\n",
       "scrape_id                                         1\n",
       "requires_license                                  1\n",
       "calendar_last_scraped                             1\n",
       "last_scraped                                      1\n",
       "is_business_travel_ready                          1\n",
       "country                                           1\n",
       "experiences_offered                               1\n",
       "instant_bookable                                  2\n",
       "is_location_exact                                 2\n",
       "market                                            2\n",
       "require_guest_profile_picture                     2\n",
       "host_identity_verified                            2\n",
       "host_has_profile_pic                              2\n",
       "host_is_superhost                                 2\n",
       "require_guest_phone_verification                  2\n",
       "room_type                                         3\n",
       "host_response_time                                4\n",
       "bed_type                                          5\n",
       "calculated_host_listings_count_shared_rooms       6\n",
       "cancellation_policy                               6\n",
       "                                               ... \n",
       "host_location                                   236\n",
       "number_of_reviews                               300\n",
       "availability_365                                366\n",
       "price                                           398\n",
       "last_review                                     757\n",
       "reviews_per_month                               777\n",
       "first_review                                   1543\n",
       "host_name                                      1575\n",
       "host_about                                     1596\n",
       "host_since                                     1712\n",
       "notes                                          2232\n",
       "interaction                                    2353\n",
       "house_rules                                    2400\n",
       "access                                         2548\n",
       "neighborhood_overview                          2563\n",
       "transit                                        2757\n",
       "host_picture_url                               2778\n",
       "host_thumbnail_url                             2778\n",
       "host_id                                        2784\n",
       "host_url                                       2784\n",
       "space                                          3494\n",
       "summary                                        4888\n",
       "amenities                                      4980\n",
       "description                                    5311\n",
       "name                                           5901\n",
       "picture_url                                    6000\n",
       "longitude                                      6150\n",
       "latitude                                       6150\n",
       "listing_url                                    6155\n",
       "id                                             6155\n",
       "Length: 106, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of unique values for each feature\n",
    "listings.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'latitude', 'minimum_maximum_nights', 'id', 'calculated_host_listings_count_entire_homes', 'availability_30', 'minimum_nights_avg_ntm', 'extra_people', 'picture_url', 'maximum_nights', 'scrape_id', 'country', 'availability_365', 'availability_60', 'minimum_nights', 'maximum_maximum_nights', 'listing_url', 'calculated_host_listings_count', 'maximum_minimum_nights', 'host_id', 'street', 'accommodates', 'requires_license', 'guests_included', 'number_of_reviews', 'longitude', 'host_url', 'experiences_offered', 'bed_type', 'availability_90', 'number_of_reviews_ltm', 'host_verifications', 'amenities', 'require_guest_phone_verification', 'country_code', 'require_guest_profile_picture', 'is_location_exact', 'has_availability', 'calendar_updated', 'maximum_nights_avg_ntm', 'is_business_travel_ready', 'cancellation_policy', 'property_type', 'neighbourhood_cleansed', 'instant_bookable', 'calendar_last_scraped', 'minimum_minimum_nights', 'last_scraped', 'price', 'room_type', 'smart_location', 'calculated_host_listings_count_shared_rooms', 'calculated_host_listings_count_private_rooms'}\n"
     ]
    }
   ],
   "source": [
    "#Providing a set of columns with 0 missing values.\n",
    "listings_no_nulls = set(listings.columns[listings.isnull().mean()==0]) \n",
    "print(listings_no_nulls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'xl_picture_url', 'square_feet', 'weekly_price', 'jurisdiction_names', 'thumbnail_url', 'license', 'monthly_price', 'neighbourhood_group_cleansed', 'host_acceptance_rate', 'medium_url'}\n"
     ]
    }
   ],
   "source": [
    "#Providing a set of columns with more than 75% of the values missing\n",
    "listings_most_nulls = set(listings.columns[listings.isnull().mean()> 0.75]) \n",
    "print(listings_most_nulls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Based on the previous data exploration, the next cell will set only the features that would be useful for our analysis. The features **'xl_picture_url', 'square_feet', 'weekly_price', 'jurisdiction_names', 'thumbnail_url', 'license', 'monthly_price', 'neighbourhood_group_cleansed', 'host_acceptance_rate', and 'medium_url'** were dropped because more than 75% of the values are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#setting the chosen 20 features for the analysis\n",
    "listings_columns = ['id', 'host_name', 'host_response_rate', \n",
    "               'host_is_superhost', 'neighbourhood_cleansed',\n",
    "               'zipcode', 'property_type', 'room_type',\n",
    "               'accommodates', 'bathrooms', 'bedrooms',\n",
    "               'beds', 'amenities', 'price', 'cleaning_fee',\n",
    "               'number_of_reviews', 'review_scores_rating', 'instant_bookable',\n",
    "               'cancellation_policy', 'reviews_per_month', 'latitude', 'longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6155, 22)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>...</th>\n",
       "      <th>amenities</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>8381472</td>\n",
       "      <td>Nick</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>2118</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>$400.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.343095</td>\n",
       "      <td>-71.079798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5166</th>\n",
       "      <td>28586005</td>\n",
       "      <td>Domio</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>North End</td>\n",
       "      <td>2113</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>$129.00</td>\n",
       "      <td>$120.00</td>\n",
       "      <td>13</td>\n",
       "      <td>97.0</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>3.28</td>\n",
       "      <td>42.365550</td>\n",
       "      <td>-71.056143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>31397068</td>\n",
       "      <td>Steve</td>\n",
       "      <td>94%</td>\n",
       "      <td>f</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>2130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...</td>\n",
       "      <td>$300.00</td>\n",
       "      <td>$165.00</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>1.00</td>\n",
       "      <td>42.321790</td>\n",
       "      <td>-71.109635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2021483</td>\n",
       "      <td>Shannon</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>South Boston</td>\n",
       "      <td>2127</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>$500.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>0.16</td>\n",
       "      <td>42.335352</td>\n",
       "      <td>-71.047413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>9853959</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>2118</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>$195.00</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.341212</td>\n",
       "      <td>-71.079551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>27549840</td>\n",
       "      <td>Sean</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>North End</td>\n",
       "      <td>2109</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...</td>\n",
       "      <td>$160.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>1.01</td>\n",
       "      <td>42.365607</td>\n",
       "      <td>-71.053421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5674</th>\n",
       "      <td>30180140</td>\n",
       "      <td>Evon</td>\n",
       "      <td>98%</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>2116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Breakfast,...</td>\n",
       "      <td>$149.00</td>\n",
       "      <td>$69.00</td>\n",
       "      <td>15</td>\n",
       "      <td>95.0</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "      <td>5.77</td>\n",
       "      <td>42.352685</td>\n",
       "      <td>-71.063872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>27733061</td>\n",
       "      <td>Aziz</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>2116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Breakfast,...</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>38</td>\n",
       "      <td>97.0</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>7.08</td>\n",
       "      <td>42.350248</td>\n",
       "      <td>-71.085469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>23272168</td>\n",
       "      <td>Bluebird</td>\n",
       "      <td>76%</td>\n",
       "      <td>f</td>\n",
       "      <td>South Boston</td>\n",
       "      <td>2127</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>$279.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>1.00</td>\n",
       "      <td>42.336815</td>\n",
       "      <td>-71.036718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>377474</td>\n",
       "      <td>Hermina</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>2122</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>$495.00</td>\n",
       "      <td>$150.00</td>\n",
       "      <td>22</td>\n",
       "      <td>87.0</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>0.28</td>\n",
       "      <td>42.298390</td>\n",
       "      <td>-71.059312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id host_name host_response_rate host_is_superhost  \\\n",
       "1057   8381472      Nick                NaN                 f   \n",
       "5166  28586005     Domio               100%                 f   \n",
       "5997  31397068     Steve                94%                 f   \n",
       "291    2021483   Shannon               100%                 t   \n",
       "1227   9853959  Margaret                NaN                 f   \n",
       "4908  27549840      Sean               100%                 f   \n",
       "5674  30180140      Evon                98%                 f   \n",
       "4964  27733061      Aziz               100%                 t   \n",
       "3991  23272168  Bluebird                76%                 f   \n",
       "103     377474   Hermina               100%                 f   \n",
       "\n",
       "     neighbourhood_cleansed zipcode property_type        room_type  \\\n",
       "1057              South End    2118     Apartment  Entire home/apt   \n",
       "5166              North End    2113     Apartment  Entire home/apt   \n",
       "5997          Jamaica Plain    2130     Apartment  Entire home/apt   \n",
       "291            South Boston    2127   Condominium  Entire home/apt   \n",
       "1227              South End    2118     Apartment  Entire home/apt   \n",
       "4908              North End    2109     Apartment  Entire home/apt   \n",
       "5674               Downtown    2116     Apartment  Entire home/apt   \n",
       "4964               Back Bay    2116     Apartment     Private room   \n",
       "3991           South Boston    2127     Apartment  Entire home/apt   \n",
       "103              Dorchester    2122         House  Entire home/apt   \n",
       "\n",
       "      accommodates  bathrooms  ...  \\\n",
       "1057             4        2.0  ...   \n",
       "5166             6        1.0  ...   \n",
       "5997             8        1.0  ...   \n",
       "291              6        2.0  ...   \n",
       "1227             4        1.0  ...   \n",
       "4908             3        1.0  ...   \n",
       "5674             4        1.0  ...   \n",
       "4964             2        1.0  ...   \n",
       "3991             5        2.0  ...   \n",
       "103             10        1.5  ...   \n",
       "\n",
       "                                              amenities     price  \\\n",
       "1057  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  $400.00    \n",
       "5166  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  $129.00    \n",
       "5997  {Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...  $300.00    \n",
       "291   {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  $500.00    \n",
       "1227  {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  $195.00    \n",
       "4908  {TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...  $160.00    \n",
       "5674  {TV,Wifi,\"Air conditioning\",Kitchen,Breakfast,...  $149.00    \n",
       "4964  {TV,Wifi,\"Air conditioning\",Kitchen,Breakfast,...  $125.00    \n",
       "3991  {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  $279.00    \n",
       "103   {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  $495.00    \n",
       "\n",
       "     cleaning_fee number_of_reviews review_scores_rating  instant_bookable  \\\n",
       "1057          NaN                 0                  NaN                 f   \n",
       "5166     $120.00                 13                 97.0                 f   \n",
       "5997     $165.00                  1                100.0                 t   \n",
       "291      $100.00                  5                 96.0                 f   \n",
       "1227      $25.00                  0                  NaN                 f   \n",
       "4908          NaN                 6                 96.0                 t   \n",
       "5674      $69.00                 15                 95.0                 t   \n",
       "4964      $50.00                 38                 97.0                 t   \n",
       "3991     $100.00                  1                100.0                 t   \n",
       "103      $150.00                 22                 87.0                 f   \n",
       "\n",
       "              cancellation_policy reviews_per_month   latitude  longitude  \n",
       "1057                     flexible               NaN  42.343095 -71.079798  \n",
       "5166  strict_14_with_grace_period              3.28  42.365550 -71.056143  \n",
       "5997  strict_14_with_grace_period              1.00  42.321790 -71.109635  \n",
       "291   strict_14_with_grace_period              0.16  42.335352 -71.047413  \n",
       "1227  strict_14_with_grace_period               NaN  42.341212 -71.079551  \n",
       "4908                     flexible              1.01  42.365607 -71.053421  \n",
       "5674                     flexible              5.77  42.352685 -71.063872  \n",
       "4964                     moderate              7.08  42.350248 -71.085469  \n",
       "3991  strict_14_with_grace_period              1.00  42.336815 -71.036718  \n",
       "103   strict_14_with_grace_period              0.28  42.298390 -71.059312  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirming the final dataset has only the chosen 22 features\n",
    "df_listings = listings[listings_columns]\n",
    "print(df_listings.shape)\n",
    "df_listings.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          int64\n",
       "host_name                  object\n",
       "host_response_rate         object\n",
       "host_is_superhost          object\n",
       "neighbourhood_cleansed     object\n",
       "zipcode                    object\n",
       "property_type              object\n",
       "room_type                  object\n",
       "accommodates                int64\n",
       "bathrooms                 float64\n",
       "bedrooms                  float64\n",
       "beds                      float64\n",
       "amenities                  object\n",
       "price                      object\n",
       "cleaning_fee               object\n",
       "number_of_reviews           int64\n",
       "review_scores_rating      float64\n",
       "instant_bookable           object\n",
       "cancellation_policy        object\n",
       "reviews_per_month         float64\n",
       "latitude                  float64\n",
       "longitude                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#investgating the data type for each feature\n",
    "df_listings.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Some of the final features need to be converted to the correct data type, which will be addressed later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "review_scores_rating      1299\n",
       "reviews_per_month         1244\n",
       "host_response_rate        1238\n",
       "cleaning_fee               979\n",
       "zipcode                     42\n",
       "bedrooms                     6\n",
       "bathrooms                    5\n",
       "beds                         3\n",
       "host_name                    2\n",
       "host_is_superhost            2\n",
       "room_type                    0\n",
       "neighbourhood_cleansed       0\n",
       "property_type                0\n",
       "longitude                    0\n",
       "accommodates                 0\n",
       "latitude                     0\n",
       "amenities                    0\n",
       "price                        0\n",
       "number_of_reviews            0\n",
       "instant_bookable             0\n",
       "cancellation_policy          0\n",
       "id                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of nulls for each chosen feature\n",
    "print(df_listings.isnull().sum().any())\n",
    "df_listings.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Again, some of the features have missing values. A further investigation will be applied to decide if we need to either remove or impute the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant_bookable             2\n",
       "host_is_superhost            2\n",
       "room_type                    3\n",
       "cancellation_policy          6\n",
       "bedrooms                     9\n",
       "bathrooms                   12\n",
       "accommodates                16\n",
       "beds                        16\n",
       "property_type               21\n",
       "neighbourhood_cleansed      25\n",
       "zipcode                     41\n",
       "host_response_rate          43\n",
       "review_scores_rating        45\n",
       "cleaning_fee               148\n",
       "number_of_reviews          300\n",
       "price                      398\n",
       "reviews_per_month          777\n",
       "host_name                 1575\n",
       "amenities                 4980\n",
       "longitude                 6150\n",
       "latitude                  6150\n",
       "id                        6155\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of unique values for each chosen feature\n",
    "df_listings.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "#### 2.1.1 Quality Issues\n",
    "\n",
    "- Missing data in some of the final 22 chosen features\n",
    "- Erroneous datatypes in prices and dates features\n",
    "- Source types in amenities where fields intricate with symbols \n",
    "\n",
    "\n",
    "#### 2.1.2 Tidiness Issues\n",
    "- Features with more than one representation: room_type, amenities, and neighbourhood_cleansed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "### 2.2 reviews data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(199330, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121665</th>\n",
       "      <td>15582062</td>\n",
       "      <td>209963762</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>7078806</td>\n",
       "      <td>Lori</td>\n",
       "      <td>If you know Boston Back Bay, you'll really app...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98580</th>\n",
       "      <td>13296735</td>\n",
       "      <td>104962035</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>56434819</td>\n",
       "      <td>Marianne</td>\n",
       "      <td>Ron and Ignacio are adorable hosts, they did t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69803</th>\n",
       "      <td>7692933</td>\n",
       "      <td>95225584</td>\n",
       "      <td>2016-08-18</td>\n",
       "      <td>2157137</td>\n",
       "      <td>Alis</td>\n",
       "      <td>Had a very serene and comfortable stay at Phyl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174463</th>\n",
       "      <td>22327139</td>\n",
       "      <td>258125311</td>\n",
       "      <td>2018-04-28</td>\n",
       "      <td>182377265</td>\n",
       "      <td>Chris</td>\n",
       "      <td>All and all this is a must stay if your visiti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114202</th>\n",
       "      <td>15044173</td>\n",
       "      <td>340020707</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>218638155</td>\n",
       "      <td>Joshua</td>\n",
       "      <td>This apartment rocks. It is a beautiful space,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13277</th>\n",
       "      <td>815639</td>\n",
       "      <td>209572469</td>\n",
       "      <td>2017-11-05</td>\n",
       "      <td>113911928</td>\n",
       "      <td>Heather</td>\n",
       "      <td>This place was perfect. It's in a great locati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111627</th>\n",
       "      <td>14868157</td>\n",
       "      <td>332523712</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>93661842</td>\n",
       "      <td>Gill</td>\n",
       "      <td>Lori is a great host ...I enjoyed chatting wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199318</th>\n",
       "      <td>31690021</td>\n",
       "      <td>409724312</td>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>67259979</td>\n",
       "      <td>Yerzhan</td>\n",
       "      <td>It’s perfect place to stay. Very clean and nic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107846</th>\n",
       "      <td>14483758</td>\n",
       "      <td>151604995</td>\n",
       "      <td>2017-05-13</td>\n",
       "      <td>21468084</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>Matthew is a responsive host and the apartment...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178609</th>\n",
       "      <td>23170412</td>\n",
       "      <td>248438357</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2441749</td>\n",
       "      <td>Eric And Susan</td>\n",
       "      <td>Such a cool 1 br apartment, and the location w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        listing_id         id        date  reviewer_id   reviewer_name  \\\n",
       "121665    15582062  209963762  2017-11-06      7078806            Lori   \n",
       "98580     13296735  104962035  2016-09-29     56434819        Marianne   \n",
       "69803      7692933   95225584  2016-08-18      2157137            Alis   \n",
       "174463    22327139  258125311  2018-04-28    182377265           Chris   \n",
       "114202    15044173  340020707  2018-10-22    218638155          Joshua   \n",
       "13277       815639  209572469  2017-11-05    113911928         Heather   \n",
       "111627    14868157  332523712  2018-10-05     93661842            Gill   \n",
       "199318    31690021  409724312  2019-02-08     67259979         Yerzhan   \n",
       "107846    14483758  151604995  2017-05-13     21468084           Bryan   \n",
       "178609    23170412  248438357  2018-03-31      2441749  Eric And Susan   \n",
       "\n",
       "                                                 comments  \n",
       "121665  If you know Boston Back Bay, you'll really app...  \n",
       "98580   Ron and Ignacio are adorable hosts, they did t...  \n",
       "69803   Had a very serene and comfortable stay at Phyl...  \n",
       "174463  All and all this is a must stay if your visiti...  \n",
       "114202  This apartment rocks. It is a beautiful space,...  \n",
       "13277   This place was perfect. It's in a great locati...  \n",
       "111627  Lori is a great host ...I enjoyed chatting wit...  \n",
       "199318  It’s perfect place to stay. Very clean and nic...  \n",
       "107846  Matthew is a responsive host and the apartment...  \n",
       "178609  Such a cool 1 br apartment, and the location w...  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the dataset\n",
    "print(reviews.shape)\n",
    "reviews.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "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>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>reviewer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.993300e+05</td>\n",
       "      <td>1.993300e+05</td>\n",
       "      <td>1.993300e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.258614e+07</td>\n",
       "      <td>2.083162e+08</td>\n",
       "      <td>7.268836e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.224075e+06</td>\n",
       "      <td>1.076633e+08</td>\n",
       "      <td>6.192693e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.781000e+03</td>\n",
       "      <td>1.021000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.924009e+06</td>\n",
       "      <td>1.154105e+08</td>\n",
       "      <td>2.073139e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.339342e+07</td>\n",
       "      <td>2.135150e+08</td>\n",
       "      <td>5.409988e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.930943e+07</td>\n",
       "      <td>3.024042e+08</td>\n",
       "      <td>1.167099e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.216361e+07</td>\n",
       "      <td>4.097567e+08</td>\n",
       "      <td>2.414605e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id            id   reviewer_id\n",
       "count  1.993300e+05  1.993300e+05  1.993300e+05\n",
       "mean   1.258614e+07  2.083162e+08  7.268836e+07\n",
       "std    8.224075e+06  1.076633e+08  6.192693e+07\n",
       "min    3.781000e+03  1.021000e+03  1.000000e+00\n",
       "25%    4.924009e+06  1.154105e+08  2.073139e+07\n",
       "50%    1.339342e+07  2.135150e+08  5.409988e+07\n",
       "75%    1.930943e+07  3.024042e+08  1.167099e+08\n",
       "max    3.216361e+07  4.097567e+08  2.414605e+08"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of each column\n",
    "reviews.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199330 entries, 0 to 199329\n",
      "Data columns (total 6 columns):\n",
      "listing_id       199330 non-null int64\n",
      "id               199330 non-null int64\n",
      "date             199330 non-null object\n",
      "reviewer_id      199330 non-null int64\n",
      "reviewer_name    199330 non-null object\n",
      "comments         199242 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 9.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#information about each feature\n",
    "reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "comments         88\n",
       "reviewer_name     0\n",
       "reviewer_id       0\n",
       "date              0\n",
       "id                0\n",
       "listing_id        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of nulls for each column\n",
    "print(reviews.isnull().sum().any())\n",
    "reviews.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "#### 2.2.1 Quality Issues\n",
    "\n",
    "- Missing data in comments\n",
    "- Erroneous datatypes in ids and dates\n",
    "- Source types in comments reviewer_name where some fields intricate with symbols and some are written in different languages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "### 2.3 calendar data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2246575, 7)\n"
     ]
    },
    {
     "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",
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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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>816327</th>\n",
       "      <td>16030858</td>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>f</td>\n",
       "      <td>$45.00</td>\n",
       "      <td>$45.00</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744053</th>\n",
       "      <td>26453232</td>\n",
       "      <td>2019-07-09</td>\n",
       "      <td>t</td>\n",
       "      <td>$150.00</td>\n",
       "      <td>$150.00</td>\n",
       "      <td>32</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670522</th>\n",
       "      <td>25871107</td>\n",
       "      <td>2019-11-07</td>\n",
       "      <td>f</td>\n",
       "      <td>$283.00</td>\n",
       "      <td>$283.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383168</th>\n",
       "      <td>8336364</td>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>f</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879390</th>\n",
       "      <td>28546173</td>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>t</td>\n",
       "      <td>$533.00</td>\n",
       "      <td>$533.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100768</th>\n",
       "      <td>916123</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>t</td>\n",
       "      <td>$199.00</td>\n",
       "      <td>$199.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695254</th>\n",
       "      <td>14950476</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>f</td>\n",
       "      <td>$307.00</td>\n",
       "      <td>$307.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967017</th>\n",
       "      <td>29379824</td>\n",
       "      <td>2019-04-19</td>\n",
       "      <td>f</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891809</th>\n",
       "      <td>28386218</td>\n",
       "      <td>2019-07-16</td>\n",
       "      <td>f</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>$60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2176426</th>\n",
       "      <td>30153933</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>f</td>\n",
       "      <td>$135.00</td>\n",
       "      <td>$135.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id        date available    price adjusted_price  \\\n",
       "816327     16030858  2019-08-09         f   $45.00         $45.00   \n",
       "1744053    26453232  2019-07-09         t  $150.00        $150.00   \n",
       "1670522    25871107  2019-11-07         f  $283.00        $283.00   \n",
       "383168      8336364  2019-10-17         f  $100.00        $100.00   \n",
       "1879390    28546173  2019-09-21         t  $533.00        $533.00   \n",
       "100768       916123  2019-11-01         t  $199.00        $199.00   \n",
       "695254     14950476  2019-03-02         f  $307.00        $307.00   \n",
       "1967017    29379824  2019-04-19         f   $50.00         $50.00   \n",
       "1891809    28386218  2019-07-16         f   $60.00         $60.00   \n",
       "2176426    30153933  2019-10-31         f  $135.00        $135.00   \n",
       "\n",
       "         minimum_nights  maximum_nights  \n",
       "816327                3             365  \n",
       "1744053              32            1125  \n",
       "1670522               3            1125  \n",
       "383168                2            1125  \n",
       "1879390               3            1125  \n",
       "100768                1            1125  \n",
       "695254                2            1125  \n",
       "1967017               2               7  \n",
       "1891809               2            1125  \n",
       "2176426               1            1125  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the dataset\n",
    "print(calendar.shape)\n",
    "calendar.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "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>listing_id</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.246575e+06</td>\n",
       "      <td>2.246575e+06</td>\n",
       "      <td>2.246575e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.879144e+07</td>\n",
       "      <td>6.885056e+00</td>\n",
       "      <td>1.698545e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.802044e+06</td>\n",
       "      <td>4.036776e+01</td>\n",
       "      <td>1.274523e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.781000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.278406e+07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.050000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.043183e+07</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.125000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.601346e+07</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.125000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.224338e+07</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>1.000000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id  minimum_nights  maximum_nights\n",
       "count  2.246575e+06    2.246575e+06    2.246575e+06\n",
       "mean   1.879144e+07    6.885056e+00    1.698545e+04\n",
       "std    8.802044e+06    4.036776e+01    1.274523e+06\n",
       "min    3.781000e+03    1.000000e+00    1.000000e+00\n",
       "25%    1.278406e+07    1.000000e+00    1.050000e+02\n",
       "50%    2.043183e+07    2.000000e+00    1.125000e+03\n",
       "75%    2.601346e+07    3.000000e+00    1.125000e+03\n",
       "max    3.224338e+07    9.990000e+02    1.000000e+08"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of each column\n",
    "calendar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "maximum_nights    0\n",
       "minimum_nights    0\n",
       "adjusted_price    0\n",
       "price             0\n",
       "available         0\n",
       "date              0\n",
       "listing_id        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of nulls for each column\n",
    "print(calendar.isnull().sum().any())\n",
    "calendar.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2246575 entries, 0 to 2246574\n",
      "Data columns (total 7 columns):\n",
      "listing_id        int64\n",
      "date              object\n",
      "available         object\n",
      "price             object\n",
      "adjusted_price    object\n",
      "minimum_nights    int64\n",
      "maximum_nights    int64\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 120.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#information about each feature\n",
    "calendar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "#### 2.3.1 Quality Issues\n",
    "\n",
    "- Erroneous datatypes in price and data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q1. How prices for all Boston's airbnb fluctuate throughout the year 2019?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "calendar_clean = calendar.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#removing the $ sign for price, and converting the date's type to datetime\n",
    "calendar_clean['year'], calendar_clean['month'], calendar_clean['day'] = calendar_clean['date'].str.split('-',2).str\n",
    "calendar_clean.date = pd.to_datetime(calendar_clean['date'])\n",
    "calendar_clean.price = calendar_clean.price.str.replace(',','')\n",
    "calendar_clean.price = calendar_clean.price.str.replace('$','')\n",
    "calendar_clean[['price']] = calendar_clean[['price']].astype(float)\n",
    "\n",
    "#removing the columns that will not be used for this question\n",
    "calendar_clean = calendar_clean.drop(['available', 'adjusted_price', 'minimum_nights', 'maximum_nights'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1448601</th>\n",
       "      <td>23170412</td>\n",
       "      <td>2019-10-22</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448961</th>\n",
       "      <td>23272728</td>\n",
       "      <td>2019-12-20</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144385</th>\n",
       "      <td>31102316</td>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>131.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11022</th>\n",
       "      <td>1183032</td>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>175.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647863</th>\n",
       "      <td>25126538</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>525.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>06</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id       date  price  year month day\n",
       "1448601    23170412 2019-10-22  283.0  2019    10  22\n",
       "1448961    23272728 2019-12-20   99.0  2019    12  20\n",
       "2144385    31102316 2020-01-08  131.0  2020    01  08\n",
       "11022       1183032 2020-01-08  175.0  2020    01  08\n",
       "1647863    25126538 2019-06-21  525.0  2019    06  21"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the quality of the dataframe\n",
    "calendar_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>143.140812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>177.284107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>208.852136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>235.687540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>229.730376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>237.662488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>234.053924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>232.110815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>232.060769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>203.867051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>190.854999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>226.081858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>232.916714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       price\n",
       "0  2019-02-01  143.140812\n",
       "1  2019-03-01  177.284107\n",
       "2  2019-04-01  208.852136\n",
       "3  2019-05-01  235.687540\n",
       "4  2019-06-01  229.730376\n",
       "5  2019-07-01  237.662488\n",
       "6  2019-08-01  234.053924\n",
       "7  2019-09-01  232.110815\n",
       "8  2019-10-01  232.060769\n",
       "9  2019-11-01  203.867051\n",
       "10 2019-12-01  190.854999\n",
       "11 2020-01-01  226.081858\n",
       "12 2020-02-01  232.916714"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping the listings by date and then finding the average price per day\n",
    "prices = pd.DataFrame(calendar_clean.groupby(['date']).mean()['price']).reset_index()\n",
    "prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215.08826725126008"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the average of the average daily prices\n",
    "prices.price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning:\n",
      "\n",
      "plotly.graph_objs.Annotation is deprecated.\n",
      "Please replace it with one of the following more specific types\n",
      "  - plotly.graph_objs.layout.Annotation\n",
      "  - plotly.graph_objs.layout.scene.Annotation\n",
      "\n",
      "\n",
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/490.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace1 = go.Scatter(x=prices.date, y=prices.price, line = dict(color = '#7F7F7F'))\n",
    "\n",
    "data = [trace1]\n",
    "\n",
    "\n",
    "layout = {\n",
    "    'title': 'Daily Average Price Change (Use the slider for more detailed trends)',\n",
    "    'xaxis': {'title': 'Day'},\n",
    "    'yaxis': {'title': 'Average Price ($)'},\n",
    "               \n",
    "    'shapes': [\n",
    "        # Line Horizontal, average\n",
    "        {\n",
    "            'type': 'line',\n",
    "            'x0': '2019-02-09',\n",
    "            'y0': 215,\n",
    "            'x1': '2020-02-08',\n",
    "            'y1': 215,\n",
    "            'line': {\n",
    "                'color': 'black',\n",
    "                'width': 1,\n",
    "                'dash': 'dashdot',\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        # 1st highlight during Apr 9 - Apr 21\n",
    "        {\n",
    "            'type': 'rect',\n",
    "            # x-reference is assigned to the x-values\n",
    "            'xref': 'x',\n",
    "            # y-reference is assigned to the plot [0,1]\n",
    "            'yref': 'paper',\n",
    "            'x0': '2019-04-09',\n",
    "            'y0': 0,\n",
    "            'x1': '2019-04-21',\n",
    "            'y1': 1,\n",
    "            'fillcolor': '#d3d3d3',\n",
    "            'opacity': 0.3,\n",
    "            'line': {\n",
    "                'width': 0,\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        # 3nd highlight during Dec 25 - Feb 7\n",
    "        {\n",
    "            'type': 'rect',\n",
    "            'xref': 'x',\n",
    "            'yref': 'paper',\n",
    "            'x0': '2019-12-25',\n",
    "            'y0': 0,\n",
    "            'x1': '2020-02-07',\n",
    "            'y1': 1,\n",
    "            'fillcolor': '#d3d3d3',\n",
    "            'opacity': 0.3,\n",
    "            'line': {\n",
    "                'width': 0,\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "layout.update(dict(\n",
    "    annotations=[go.Annotation(text=\"Overall Average Price ($215)\", x='2019-03-15', y=215)]),\n",
    "             xaxis=dict(\n",
    "        rangeselector=dict(\n",
    "            buttons=list([\n",
    "                dict(count=1,\n",
    "                     label='1m',\n",
    "                     step='month',\n",
    "                     stepmode='backward'),\n",
    "                dict(count=6,\n",
    "                     label='6m',\n",
    "                     step='month',\n",
    "                     stepmode='backward'),\n",
    "                dict(count=1,\n",
    "                    label='YTD',\n",
    "                    step='year',\n",
    "                    stepmode='todate'),\n",
    "                dict(count=1,\n",
    "                    label='1y',\n",
    "                    step='year',\n",
    "                    stepmode='backward'),\n",
    "                dict(step='all')\n",
    "            ])\n",
    "        ),\n",
    "        rangeslider=dict(\n",
    "            visible = True\n",
    "        ),\n",
    "        type='date'\n",
    "    )\n",
    "             )\n",
    "              \n",
    "\n",
    "py.iplot({'data': data, 'layout': layout}, filename='Daily Average Price Change')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "The time series plot illustrates that we have two periods where the Airbnb listings prices increased promptly. The shaded areas show the dates when the price rises. The horizontal line represents the average renting price all year. \n",
    "\n",
    "    1. The period between April 8, 2019, and April 21, 2019, shows an increase in average prices above the overall average price. After further investigation, the increase is due to the Boston Marathon, one of the world's oldest and most challenging races. The marathon took place on Monday, April 15.\n",
    "    \n",
    "    2. The period between December 25, 2019, and February 7, 2020, shows an increase in prices, which is expected as it holds the Christmas Day and the New Year's Eve and Day.\n",
    "    \n",
    "    3. A significant difference in the average prices between February 2019 and February 2020. While in 2019 it was the lowest period with rates ranges between around $136 per night up to $200 per night, it starts from $234 per night in 2020.\n",
    "    \n",
    "    4. Overall, the off-peak period for Boston's Airbnb occurs during the months of February and March in 2019, and the peak period occurs between April and October in 2019. Prices go down in November until the trend goes up again in December."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q2. What are the peak and off-peak months during the year for airbnb rental prices in Boston?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#splitting the date to year and month, then concat the files to create monthly column\n",
    "calendar_clean['month'] = calendar_clean['year'].map(str) + \"-\" + calendar_clean['month'].map(str)\n",
    "\n",
    "#convertting the month to date\n",
    "calendar_clean.date = pd.to_datetime(calendar_clean['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "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>month</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02</td>\n",
       "      <td>143.140812</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-03</td>\n",
       "      <td>177.284107</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04</td>\n",
       "      <td>208.852136</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05</td>\n",
       "      <td>235.687540</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06</td>\n",
       "      <td>229.730376</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07</td>\n",
       "      <td>237.662488</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-08</td>\n",
       "      <td>234.053924</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09</td>\n",
       "      <td>232.110815</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-10</td>\n",
       "      <td>232.060769</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-11</td>\n",
       "      <td>203.867051</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>190.854999</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01</td>\n",
       "      <td>226.081858</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>232.916714</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5369.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month        mean   min     max\n",
       "0   2019-02  143.140812  10.0  5000.0\n",
       "1   2019-03  177.284107  10.0  7132.0\n",
       "2   2019-04  208.852136  10.0  5000.0\n",
       "3   2019-05  235.687540  10.0  9999.0\n",
       "4   2019-06  229.730376  10.0  5000.0\n",
       "5   2019-07  237.662488  10.0  5000.0\n",
       "6   2019-08  234.053924  10.0  5000.0\n",
       "7   2019-09  232.110815  10.0  6414.0\n",
       "8   2019-10  232.060769  10.0  5407.0\n",
       "9   2019-11  203.867051  10.0  5000.0\n",
       "10  2019-12  190.854999  10.0  5000.0\n",
       "11  2020-01  226.081858  10.0  5000.0\n",
       "12  2020-02  232.916714  10.0  5369.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_trend = calendar_clean.groupby('month').describe()['price'].reset_index()\n",
    "month_trend = month_trend.drop(['count', 'std', '25%', '50%', '75%'], axis=1)\n",
    "month_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/488.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "trace_mean = go.Scatter(\n",
    "    x=month_trend.month,\n",
    "    y=month_trend['mean'],\n",
    "    name = \"Average Price Per month\",\n",
    "    line = dict(color = '#7F7F7F'),\n",
    "    opacity = 0.8)\n",
    "\n",
    "data = [trace_mean]\n",
    "\n",
    "layout = {\n",
    "    'title': 'Monthly Price Trend',\n",
    "    'xaxis': {'title': 'Month'},\n",
    "    'yaxis': {'title': 'Average Price ($)'},\n",
    "               \n",
    "    'shapes': [\n",
    "        # Line Horizontal, average\n",
    "        {\n",
    "            'type': 'line',\n",
    "            'x0': '2019-02-01',\n",
    "            'y0': 215,\n",
    "            'x1': '2020-02-01',\n",
    "            'y1': 215,\n",
    "            'line': {\n",
    "                'color': 'black',\n",
    "                'width': 1,\n",
    "                'dash': 'dashdot',\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        # 1st highlight above average months\n",
    "        {\n",
    "            'type': 'rect',\n",
    "            # x-reference is assigned to the x-values\n",
    "            'xref': 'x',\n",
    "            # y-reference is assigned to the plot [0,1]\n",
    "            'yref': 'y',\n",
    "            'x0': '2019-02',\n",
    "            'y0': 215,\n",
    "            'x1': '2020-02',\n",
    "            'y1': 240,\n",
    "            'fillcolor': 'tomato',\n",
    "            'opacity': 0.1,\n",
    "            'line': {\n",
    "                'width': 0,\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        # 3nd highlight below average months\n",
    "        {\n",
    "            'type': 'rect',\n",
    "            'xref': 'x',\n",
    "            'yref': 'y',\n",
    "            'x0': '2019-02',\n",
    "            'y0': 215,\n",
    "            'x1': '2020-02',\n",
    "            'y1': 140,\n",
    "            'fillcolor': 'olive',\n",
    "            'opacity': 0.1,\n",
    "            'line': {\n",
    "                'width': 0,\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "layout.update(dict(annotations=[go.Annotation(text=\"Overall Average Price ($215)\", x='2019-03-15', y=215)]))\n",
    "        \n",
    "fig = dict(data=data, layout=layout)\n",
    "py.iplot(fig, filename = \"Monthly Price Trend\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "The monthly price trend plot shows that the peak and off-peak months for Boston Airbnb listings' prices. The red-colored areas show the months when the average price per night is above the overall average price for all listings per night, while the green-colored area represents the off-peak months when the average price per night is below the overall average rate for all listings per night.\n",
    "\n",
    "    1. February, March, the beginning of April, November, and most of December in 2019 are the off-peak months when prices are below $215 per nights. \n",
    "    \n",
    "    2. The second half of April, May, July, August, September, the first half of October, end of December in 2019 along with January and February in 2010 are the peak months when prices are above $215 per night."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "# <center>STAGE TWO</center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "## 3. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#copying the latest dataset\n",
    "df_listings_prep1 = df_listings.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#dropping the features with many number of missing data\n",
    "df_listings_prep1 = df_listings_prep1.drop(['review_scores_rating', 'host_response_rate', 'reviews_per_month'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, as we found earlier, the features **'review_scores_rating', 'reviews_per_month', and 'host_response_rate'** have more than 1200 missing values out of 6155. In my opnion, the columns are not associated with sensitive information that would impact our analysis. Therefore, instead of droping the listings with these missing values or imputing the numbers, I decided to drop the feature as they will not be used in answering the business question or creating the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          int64\n",
       "host_name                  object\n",
       "host_is_superhost          object\n",
       "neighbourhood_cleansed     object\n",
       "zipcode                    object\n",
       "property_type              object\n",
       "room_type                  object\n",
       "accommodates                int64\n",
       "bathrooms                 float64\n",
       "bedrooms                  float64\n",
       "beds                      float64\n",
       "amenities                  object\n",
       "price                      object\n",
       "cleaning_fee               object\n",
       "number_of_reviews           int64\n",
       "instant_bookable           object\n",
       "cancellation_policy        object\n",
       "latitude                  float64\n",
       "longitude                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#investgating the data type for each feature\n",
    "df_listings_prep1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#converting accommodates' type to float\n",
    "df_listings_prep1.accommodates = df_listings_prep1.accommodates.astype(float)\n",
    "\n",
    "#converting number_of_reviews' type to float\n",
    "df_listings_prep1.number_of_reviews = df_listings_prep1.number_of_reviews.astype(float)\n",
    "\n",
    "#confirming zipcodes are all set with 5 numbers\n",
    "df_listings_prep1.zipcode = df_listings_prep1.zipcode.astype(str).str.zfill(5)\n",
    "\n",
    "#removing the $ sign for price and cleaning_fee, then converting the types to float\n",
    "\n",
    "df_listings_prep1.price = df_listings_prep1.price.str.replace('$','')\n",
    "df_listings_prep1.price = df_listings_prep1.price.str.replace(',','')\n",
    "df_listings_prep1.cleaning_fee = df_listings_prep1.cleaning_fee.str.replace('$','')\n",
    "df_listings_prep1.cleaning_fee = df_listings_prep1.cleaning_fee.str.replace(',','')\n",
    "df_listings_prep1[['price', 'cleaning_fee']] = df_listings_prep1[['price', 'cleaning_fee']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     9\n",
       "float64    9\n",
       "int64      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assessing the number of each data type\n",
    "df_listings_prep1.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3781</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.365241</td>\n",
       "      <td>-71.029361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5506</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>42.329808</td>\n",
       "      <td>-71.095595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6695</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>42.329941</td>\n",
       "      <td>-71.093505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6976</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.292438</td>\n",
       "      <td>-71.135765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8789</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>42.359187</td>\n",
       "      <td>-71.062651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8792</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>42.358497</td>\n",
       "      <td>-71.062011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9765</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>42.342594</td>\n",
       "      <td>-71.079421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9824</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>42.349496</td>\n",
       "      <td>-71.085954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9827</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.352149</td>\n",
       "      <td>-71.063301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9855</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.343371</td>\n",
       "      <td>-71.098708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9857</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>42.354290</td>\n",
       "      <td>-71.072772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9858</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.344471</td>\n",
       "      <td>-71.081786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9859</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.350260</td>\n",
       "      <td>-71.084718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9860</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>42.354115</td>\n",
       "      <td>-71.074414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9870</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.344227</td>\n",
       "      <td>-71.077878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10730</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>42.358403</td>\n",
       "      <td>-71.061850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10811</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.349730</td>\n",
       "      <td>-71.087924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10813</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.349613</td>\n",
       "      <td>-71.089038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10986</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.363520</td>\n",
       "      <td>-71.050754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13589</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>42.355263</td>\n",
       "      <td>-71.072793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13592</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.353763</td>\n",
       "      <td>-71.074239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18711</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.323286</td>\n",
       "      <td>-71.062103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21891</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>42.352179</td>\n",
       "      <td>-71.063160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22208</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.343210</td>\n",
       "      <td>-71.077760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22354</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>42.344957</td>\n",
       "      <td>-71.074857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23370</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.346091</td>\n",
       "      <td>-71.078578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23668</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.363865</td>\n",
       "      <td>-71.068950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>24240</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>42.363660</td>\n",
       "      <td>-71.050232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>25142</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>42.344865</td>\n",
       "      <td>-71.102600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>25418</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.364945</td>\n",
       "      <td>-71.067668</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6125</th>\n",
       "      <td>32116807</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.321519</td>\n",
       "      <td>-71.110608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6126</th>\n",
       "      <td>32125673</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.362714</td>\n",
       "      <td>-71.131194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>32127951</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.373256</td>\n",
       "      <td>-71.032211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6128</th>\n",
       "      <td>32133411</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.326400</td>\n",
       "      <td>-71.092240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>32133771</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.359055</td>\n",
       "      <td>-71.052996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>32138377</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.354935</td>\n",
       "      <td>-71.165600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6131</th>\n",
       "      <td>32141341</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.325101</td>\n",
       "      <td>-71.105237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6132</th>\n",
       "      <td>32147828</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.365379</td>\n",
       "      <td>-71.069163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6133</th>\n",
       "      <td>32155120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.288390</td>\n",
       "      <td>-71.050601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6134</th>\n",
       "      <td>32158419</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.331076</td>\n",
       "      <td>-71.110691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6135</th>\n",
       "      <td>32161143</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.360359</td>\n",
       "      <td>-71.066792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136</th>\n",
       "      <td>32162031</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.287114</td>\n",
       "      <td>-71.092115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6137</th>\n",
       "      <td>32163609</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.361173</td>\n",
       "      <td>-71.070311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6138</th>\n",
       "      <td>32163985</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.327272</td>\n",
       "      <td>-71.080344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6139</th>\n",
       "      <td>32168098</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.319170</td>\n",
       "      <td>-71.105199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6140</th>\n",
       "      <td>32172713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.330545</td>\n",
       "      <td>-71.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141</th>\n",
       "      <td>32180329</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.345382</td>\n",
       "      <td>-71.079692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6142</th>\n",
       "      <td>32183321</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.350709</td>\n",
       "      <td>-71.134464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6143</th>\n",
       "      <td>32188679</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.352673</td>\n",
       "      <td>-71.129030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>32195225</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.332255</td>\n",
       "      <td>-71.030875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6145</th>\n",
       "      <td>32196121</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.346257</td>\n",
       "      <td>-71.140473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>32197096</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.300713</td>\n",
       "      <td>-71.084060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6147</th>\n",
       "      <td>32202426</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.355790</td>\n",
       "      <td>-71.110322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6148</th>\n",
       "      <td>32213220</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.346640</td>\n",
       "      <td>-71.103684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>32223560</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.359254</td>\n",
       "      <td>-71.053765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6150</th>\n",
       "      <td>32234660</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.262505</td>\n",
       "      <td>-71.151490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>32238109</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.318001</td>\n",
       "      <td>-71.053537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>32241248</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.349531</td>\n",
       "      <td>-71.082424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>32242842</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.351701</td>\n",
       "      <td>-71.065359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>32243378</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.340967</td>\n",
       "      <td>-71.134066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6155 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  accommodates  bathrooms  bedrooms  beds  price  cleaning_fee  \\\n",
       "0         3781           2.0        1.0       1.0   1.0  125.0          75.0   \n",
       "1         5506           2.0        1.0       1.0   1.0  145.0          40.0   \n",
       "2         6695           4.0        1.0       1.0   2.0  169.0          70.0   \n",
       "3         6976           2.0        1.0       1.0   1.0   65.0           0.0   \n",
       "4         8789           2.0        1.0       1.0   1.0   99.0         250.0   \n",
       "5         8792           2.0        1.0       1.0   1.0  154.0         250.0   \n",
       "6         9765           2.0        1.0       NaN   1.0  229.0          75.0   \n",
       "7         9824           2.0        1.0       NaN   1.0  209.0           NaN   \n",
       "8         9827           2.0        1.0       1.0   1.0  389.0         150.0   \n",
       "9         9855           3.0        1.0       1.0   1.0  259.0         150.0   \n",
       "10        9857           4.0        1.0       2.0   2.0  342.0           NaN   \n",
       "11        9858           6.0        1.0       2.0   2.0  489.0           NaN   \n",
       "12        9859           2.0        1.0       1.0   1.0  179.0           NaN   \n",
       "13        9860           2.0        1.0       1.0   1.0  259.0           NaN   \n",
       "14        9870           2.0        1.0       1.0   1.0  279.0           NaN   \n",
       "15       10730           2.0        1.0       1.0   1.0  150.0         250.0   \n",
       "16       10811           3.0        1.0       0.0   2.0  179.0         100.0   \n",
       "17       10813           3.0        1.0       0.0   1.0  179.0         100.0   \n",
       "18       10986           2.0        1.0       0.0   1.0  115.0         145.0   \n",
       "19       13589           6.0        2.0       3.0   3.0  389.0           NaN   \n",
       "20       13592           4.0        1.5       2.0   2.0  359.0           NaN   \n",
       "21       18711           7.0        1.0       3.0   3.0  126.0         120.0   \n",
       "22       21891           4.0        1.0       2.0   2.0  289.0         200.0   \n",
       "23       22208           2.0        1.0       NaN   1.0  225.0           NaN   \n",
       "24       22354           1.0        NaN       1.0   1.0  148.0          21.0   \n",
       "25       23370           2.0        1.0       1.0   1.0  289.0         150.0   \n",
       "26       23668           2.0        1.0       1.0   1.0  249.0         150.0   \n",
       "27       24240           5.0        2.0       2.0   3.0  275.0           0.0   \n",
       "28       25142           4.0        NaN       2.0   2.0  399.0         150.0   \n",
       "29       25418           2.0        1.0       1.0   1.0  279.0         200.0   \n",
       "...        ...           ...        ...       ...   ...    ...           ...   \n",
       "6125  32116807           2.0        1.0       1.0   1.0  120.0          35.0   \n",
       "6126  32125673           2.0        1.0       1.0   1.0   60.0          15.0   \n",
       "6127  32127951           5.0        1.0       2.0   2.0  143.0          40.0   \n",
       "6128  32133411           3.0        1.5       1.0   2.0   70.0          25.0   \n",
       "6129  32133771           2.0        1.0       0.0   1.0  100.0          60.0   \n",
       "6130  32138377           4.0        1.5       3.0   3.0   65.0          20.0   \n",
       "6131  32141341           5.0        2.0       1.0   4.0  200.0          30.0   \n",
       "6132  32147828           2.0        1.0       1.0   1.0  125.0          40.0   \n",
       "6133  32155120           2.0        1.0       1.0   1.0   43.0           NaN   \n",
       "6134  32158419           4.0        1.0       2.0   3.0  160.0           NaN   \n",
       "6135  32161143           3.0        1.0       2.0   2.0  160.0           NaN   \n",
       "6136  32162031           2.0        1.0       1.0   1.0   45.0           0.0   \n",
       "6137  32163609           3.0        1.0       1.0   1.0  109.0          30.0   \n",
       "6138  32163985           4.0        1.0       2.0   2.0  199.0          25.0   \n",
       "6139  32168098           2.0        1.0       1.0   1.0   85.0           NaN   \n",
       "6140  32172713           1.0        1.0       1.0   1.0   60.0           NaN   \n",
       "6141  32180329           5.0        1.0       2.0   1.0  220.0           NaN   \n",
       "6142  32183321           2.0        1.0       1.0   1.0   59.0          25.0   \n",
       "6143  32188679           1.0        1.0       0.0   1.0   60.0           NaN   \n",
       "6144  32195225           4.0        1.5       2.0   1.0  200.0           NaN   \n",
       "6145  32196121           2.0        1.0       1.0   1.0   45.0           NaN   \n",
       "6146  32197096           4.0        1.0       1.0   2.0   75.0           NaN   \n",
       "6147  32202426           2.0        1.0       1.0   2.0   80.0           NaN   \n",
       "6148  32213220           3.0        1.0       0.0   2.0  110.0           NaN   \n",
       "6149  32223560           4.0        1.0       1.0   1.0  250.0           NaN   \n",
       "6150  32234660           6.0        1.0       1.0   3.0  125.0           NaN   \n",
       "6151  32238109           2.0        1.0       1.0   1.0   75.0           NaN   \n",
       "6152  32241248           4.0        1.0       1.0   2.0   80.0          25.0   \n",
       "6153  32242842           2.0        1.0       1.0   1.0   60.0          40.0   \n",
       "6154  32243378           3.0        1.0       1.0   1.0   66.0           8.0   \n",
       "\n",
       "      number_of_reviews   latitude  longitude  \n",
       "0                  14.0  42.365241 -71.029361  \n",
       "1                  80.0  42.329808 -71.095595  \n",
       "2                  85.0  42.329941 -71.093505  \n",
       "3                  75.0  42.292438 -71.135765  \n",
       "4                  22.0  42.359187 -71.062651  \n",
       "5                  24.0  42.358497 -71.062011  \n",
       "6                   9.0  42.342594 -71.079421  \n",
       "7                  23.0  42.349496 -71.085954  \n",
       "8                   8.0  42.352149 -71.063301  \n",
       "9                   3.0  42.343371 -71.098708  \n",
       "10                 25.0  42.354290 -71.072772  \n",
       "11                  1.0  42.344471 -71.081786  \n",
       "12                  3.0  42.350260 -71.084718  \n",
       "13                 22.0  42.354115 -71.074414  \n",
       "14                  8.0  42.344227 -71.077878  \n",
       "15                 27.0  42.358403 -71.061850  \n",
       "16                  7.0  42.349730 -71.087924  \n",
       "17                  1.0  42.349613 -71.089038  \n",
       "18                  2.0  42.363520 -71.050754  \n",
       "19                 63.0  42.355263 -71.072793  \n",
       "20                 20.0  42.353763 -71.074239  \n",
       "21                 43.0  42.323286 -71.062103  \n",
       "22                 21.0  42.352179 -71.063160  \n",
       "23                  4.0  42.343210 -71.077760  \n",
       "24                293.0  42.344957 -71.074857  \n",
       "25                  5.0  42.346091 -71.078578  \n",
       "26                  0.0  42.363865 -71.068950  \n",
       "27                 82.0  42.363660 -71.050232  \n",
       "28                  9.0  42.344865 -71.102600  \n",
       "29                  4.0  42.364945 -71.067668  \n",
       "...                 ...        ...        ...  \n",
       "6125                0.0  42.321519 -71.110608  \n",
       "6126                0.0  42.362714 -71.131194  \n",
       "6127                0.0  42.373256 -71.032211  \n",
       "6128                0.0  42.326400 -71.092240  \n",
       "6129                0.0  42.359055 -71.052996  \n",
       "6130                0.0  42.354935 -71.165600  \n",
       "6131                0.0  42.325101 -71.105237  \n",
       "6132                0.0  42.365379 -71.069163  \n",
       "6133                0.0  42.288390 -71.050601  \n",
       "6134                0.0  42.331076 -71.110691  \n",
       "6135                0.0  42.360359 -71.066792  \n",
       "6136                0.0  42.287114 -71.092115  \n",
       "6137                1.0  42.361173 -71.070311  \n",
       "6138                0.0  42.327272 -71.080344  \n",
       "6139                0.0  42.319170 -71.105199  \n",
       "6140                0.0  42.330545 -71.101546  \n",
       "6141                0.0  42.345382 -71.079692  \n",
       "6142                0.0  42.350709 -71.134464  \n",
       "6143                0.0  42.352673 -71.129030  \n",
       "6144                0.0  42.332255 -71.030875  \n",
       "6145                0.0  42.346257 -71.140473  \n",
       "6146                0.0  42.300713 -71.084060  \n",
       "6147                0.0  42.355790 -71.110322  \n",
       "6148                0.0  42.346640 -71.103684  \n",
       "6149                0.0  42.359254 -71.053765  \n",
       "6150                0.0  42.262505 -71.151490  \n",
       "6151                0.0  42.318001 -71.053537  \n",
       "6152                0.0  42.349531 -71.082424  \n",
       "6153                0.0  42.351701 -71.065359  \n",
       "6154                0.0  42.340967 -71.134066  \n",
       "\n",
       "[6155 rows x 10 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the numerical features\n",
    "features_num = df_listings_prep1.select_dtypes(include=['float', 'int'])\n",
    "features_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frank</td>\n",
       "      <td>t</td>\n",
       "      <td>East Boston</td>\n",
       "      <td>02128</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Terry</td>\n",
       "      <td>t</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Guest suite</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Terry</td>\n",
       "      <td>t</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phil</td>\n",
       "      <td>t</td>\n",
       "      <td>Roslindale</td>\n",
       "      <td>02131</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anne</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anne</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>02118</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02111</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>02215</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Michelle</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Michelle</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Michelle</td>\n",
       "      <td>f</td>\n",
       "      <td>North End</td>\n",
       "      <td>02109</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lance</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02125</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02111</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>02118</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Robert</td>\n",
       "      <td>f</td>\n",
       "      <td>South End</td>\n",
       "      <td>02116</td>\n",
       "      <td>Bed and breakfast</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Internet,Wifi,\"Air conditioning\",Heating,\"Smo...</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Gretchen</td>\n",
       "      <td>t</td>\n",
       "      <td>North End</td>\n",
       "      <td>02109</td>\n",
       "      <td>Boat</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Internet,Wifi,Kitchen,\"Paid parking off pr...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>02215</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Seamless</td>\n",
       "      <td>f</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...</td>\n",
       "      <td>f</td>\n",
       "      <td>super_strict_30</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6125</th>\n",
       "      <td>Cortney</td>\n",
       "      <td>f</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6126</th>\n",
       "      <td>Shekeyla</td>\n",
       "      <td>f</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Free parking on premises\",He...</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>f</td>\n",
       "      <td>East Boston</td>\n",
       "      <td>02128</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6128</th>\n",
       "      <td>Wendy</td>\n",
       "      <td>f</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Pets allo...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>Paige</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02109</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Paid p...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>Rachel</td>\n",
       "      <td>f</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02135</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6131</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>f</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",\"Pets allowed\",\"Free ...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6132</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>f</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,\"Free parking...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6133</th>\n",
       "      <td>Taryn</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02122</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Heating,Washe...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6134</th>\n",
       "      <td>Hesham</td>\n",
       "      <td>f</td>\n",
       "      <td>Mission Hill</td>\n",
       "      <td>02130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Pool,Kitchen,\"Free...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6135</th>\n",
       "      <td>Ekaterina</td>\n",
       "      <td>f</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>02114</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136</th>\n",
       "      <td>Mojisola</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02124</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Free parking on premises\",He...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6137</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>f</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6138</th>\n",
       "      <td>Stayhere</td>\n",
       "      <td>f</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6139</th>\n",
       "      <td>David</td>\n",
       "      <td>f</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Heating,Washer,Dryer,...</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6140</th>\n",
       "      <td>Brigith</td>\n",
       "      <td>f</td>\n",
       "      <td>Mission Hill</td>\n",
       "      <td>02120</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",\"Free parking on prem...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141</th>\n",
       "      <td>Lola</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Air conditioning\",Kitchen,Elevator,\"Indoo...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6142</th>\n",
       "      <td>Hank</td>\n",
       "      <td>f</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6143</th>\n",
       "      <td>Yaling</td>\n",
       "      <td>f</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{\"Air conditioning\",Kitchen,\"Pets allowed\",Hea...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>Dmitriy</td>\n",
       "      <td>f</td>\n",
       "      <td>South Boston</td>\n",
       "      <td>02127</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Kitchen,\"Pets allowed\",Heating,Washer,Drye...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6145</th>\n",
       "      <td>Kashyap</td>\n",
       "      <td>f</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02135</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,Kitchen,Heating,\"Family/kid friendly\",Wa...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>Alan</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02121</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6147</th>\n",
       "      <td>Barbara</td>\n",
       "      <td>f</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02139</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Breakfast,Hea...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6148</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>f</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>02215</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>Jane</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02109</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Pool,Kitchen,Eleva...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6150</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>f</td>\n",
       "      <td>West Roxbury</td>\n",
       "      <td>02132</td>\n",
       "      <td>Camper/RV</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{\"Air conditioning\",Kitchen,\"Pets allowed\",Bre...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>Courtney</td>\n",
       "      <td>f</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02125</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Pets allowed\",Breakfast,Heat...</td>\n",
       "      <td>t</td>\n",
       "      <td>flexible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>Gabi</td>\n",
       "      <td>f</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Wifi,Kitchen,Breakfast,Heating,\"Smoke dete...</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>Will</td>\n",
       "      <td>f</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,Kitchen,\"Paid parking off premises\",Heat...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>Hilal</td>\n",
       "      <td>t</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02446</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>{Wifi,Kitchen,\"Paid parking off premises\",Heat...</td>\n",
       "      <td>t</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6155 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      host_name host_is_superhost neighbourhood_cleansed zipcode  \\\n",
       "0         Frank                 t            East Boston   02128   \n",
       "1         Terry                 t                Roxbury   02119   \n",
       "2         Terry                 t                Roxbury   02119   \n",
       "3          Phil                 t             Roslindale   02131   \n",
       "4          Anne                 f               Downtown   02108   \n",
       "5          Anne                 f               Downtown   02108   \n",
       "6      Seamless                 f              South End   02118   \n",
       "7      Seamless                 f               Back Bay   02115   \n",
       "8      Seamless                 f               Downtown   02111   \n",
       "9      Seamless                 f                 Fenway   02215   \n",
       "10     Seamless                 f               Back Bay   02116   \n",
       "11     Seamless                 f               Back Bay   02116   \n",
       "12     Seamless                 f               Back Bay   02115   \n",
       "13     Seamless                 f               Back Bay   02116   \n",
       "14     Seamless                 f              South End   02116   \n",
       "15         Anne                 f               Downtown   02108   \n",
       "16     Michelle                 f               Back Bay   02115   \n",
       "17     Michelle                 f               Back Bay   02115   \n",
       "18     Michelle                 f              North End   02109   \n",
       "19     Seamless                 f               Back Bay   02116   \n",
       "20     Seamless                 f               Back Bay   02116   \n",
       "21        Lance                 f             Dorchester   02125   \n",
       "22     Seamless                 f               Downtown   02111   \n",
       "23     Seamless                 f              South End   02118   \n",
       "24       Robert                 f              South End   02116   \n",
       "25     Seamless                 f               Back Bay   02116   \n",
       "26     Seamless                 f               West End   02114   \n",
       "27     Gretchen                 t              North End   02109   \n",
       "28     Seamless                 f                 Fenway   02215   \n",
       "29     Seamless                 f               West End   02114   \n",
       "...         ...               ...                    ...     ...   \n",
       "6125    Cortney                 f          Jamaica Plain   02130   \n",
       "6126   Shekeyla                 f                Allston   02134   \n",
       "6127     Taylor                 f            East Boston   02128   \n",
       "6128      Wendy                 f                Roxbury   02119   \n",
       "6129      Paige                 f               Downtown   02109   \n",
       "6130     Rachel                 f               Brighton   02135   \n",
       "6131     Nicole                 f          Jamaica Plain   02130   \n",
       "6132     Thomas                 f               West End   02114   \n",
       "6133      Taryn                 f             Dorchester   02122   \n",
       "6134     Hesham                 f           Mission Hill   02130   \n",
       "6135  Ekaterina                 f            Beacon Hill   02114   \n",
       "6136   Mojisola                 f             Dorchester   02124   \n",
       "6137     Ashley                 f               West End   02114   \n",
       "6138   Stayhere                 f                Roxbury   02119   \n",
       "6139      David                 f          Jamaica Plain   02130   \n",
       "6140    Brigith                 f           Mission Hill   02120   \n",
       "6141       Lola                 f               Back Bay   02116   \n",
       "6142       Hank                 f                Allston   02134   \n",
       "6143     Yaling                 f                Allston   02134   \n",
       "6144    Dmitriy                 f           South Boston   02127   \n",
       "6145    Kashyap                 f               Brighton   02135   \n",
       "6146       Alan                 f             Dorchester   02121   \n",
       "6147    Barbara                 f                Allston   02139   \n",
       "6148     Taylor                 f                 Fenway   02215   \n",
       "6149       Jane                 f               Downtown   02109   \n",
       "6150     Daniel                 f           West Roxbury   02132   \n",
       "6151   Courtney                 f             Dorchester   02125   \n",
       "6152       Gabi                 f               Back Bay   02116   \n",
       "6153       Will                 f               Downtown   02116   \n",
       "6154      Hilal                 t               Brighton   02446   \n",
       "\n",
       "           property_type        room_type  \\\n",
       "0              Apartment  Entire home/apt   \n",
       "1            Guest suite  Entire home/apt   \n",
       "2            Condominium  Entire home/apt   \n",
       "3              Apartment     Private room   \n",
       "4              Apartment  Entire home/apt   \n",
       "5              Apartment  Entire home/apt   \n",
       "6              Apartment  Entire home/apt   \n",
       "7     Serviced apartment  Entire home/apt   \n",
       "8     Serviced apartment  Entire home/apt   \n",
       "9              Apartment  Entire home/apt   \n",
       "10             Apartment  Entire home/apt   \n",
       "11             Apartment  Entire home/apt   \n",
       "12    Serviced apartment  Entire home/apt   \n",
       "13    Serviced apartment  Entire home/apt   \n",
       "14    Serviced apartment  Entire home/apt   \n",
       "15             Apartment  Entire home/apt   \n",
       "16             Apartment  Entire home/apt   \n",
       "17             Apartment  Entire home/apt   \n",
       "18           Condominium  Entire home/apt   \n",
       "19    Serviced apartment  Entire home/apt   \n",
       "20             Apartment  Entire home/apt   \n",
       "21    Serviced apartment  Entire home/apt   \n",
       "22             Apartment  Entire home/apt   \n",
       "23    Serviced apartment  Entire home/apt   \n",
       "24     Bed and breakfast     Private room   \n",
       "25             Apartment  Entire home/apt   \n",
       "26    Serviced apartment  Entire home/apt   \n",
       "27                  Boat  Entire home/apt   \n",
       "28             Apartment  Entire home/apt   \n",
       "29             Apartment  Entire home/apt   \n",
       "...                  ...              ...   \n",
       "6125           Apartment     Private room   \n",
       "6126           Apartment     Private room   \n",
       "6127           Apartment  Entire home/apt   \n",
       "6128               House     Private room   \n",
       "6129           Apartment  Entire home/apt   \n",
       "6130               House     Private room   \n",
       "6131               House     Private room   \n",
       "6132           Apartment     Private room   \n",
       "6133           Apartment     Private room   \n",
       "6134           Apartment  Entire home/apt   \n",
       "6135         Condominium  Entire home/apt   \n",
       "6136           Apartment     Private room   \n",
       "6137           Apartment  Entire home/apt   \n",
       "6138           Apartment  Entire home/apt   \n",
       "6139         Condominium     Private room   \n",
       "6140               House     Private room   \n",
       "6141           Apartment  Entire home/apt   \n",
       "6142               House     Private room   \n",
       "6143           Apartment     Private room   \n",
       "6144           Apartment  Entire home/apt   \n",
       "6145           Apartment     Private room   \n",
       "6146         Condominium  Entire home/apt   \n",
       "6147               House     Private room   \n",
       "6148           Apartment  Entire home/apt   \n",
       "6149         Condominium  Entire home/apt   \n",
       "6150           Camper/RV     Private room   \n",
       "6151               House     Private room   \n",
       "6152           Apartment  Entire home/apt   \n",
       "6153           Apartment     Private room   \n",
       "6154           Apartment     Private room   \n",
       "\n",
       "                                              amenities instant_bookable  \\\n",
       "0     {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...                f   \n",
       "1     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                t   \n",
       "2     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                t   \n",
       "3     {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...                f   \n",
       "4     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "5     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "6     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "7     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "8     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "9     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "10    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "11    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "12    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "13    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "14    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "15    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "16    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "17    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "18    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "19    {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...                f   \n",
       "20    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "21    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                t   \n",
       "22    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "23    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "24    {Internet,Wifi,\"Air conditioning\",Heating,\"Smo...                t   \n",
       "25    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "26    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...                f   \n",
       "27    {TV,Internet,Wifi,Kitchen,\"Paid parking off pr...                f   \n",
       "28    {TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...                f   \n",
       "29    {TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...                f   \n",
       "...                                                 ...              ...   \n",
       "6125  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...                t   \n",
       "6126  {TV,Wifi,Kitchen,\"Free parking on premises\",He...                t   \n",
       "6127  {Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...                t   \n",
       "6128  {TV,Wifi,\"Air conditioning\",Kitchen,\"Pets allo...                t   \n",
       "6129  {TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Paid p...                t   \n",
       "6130  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...                t   \n",
       "6131  {Wifi,\"Air conditioning\",\"Pets allowed\",\"Free ...                t   \n",
       "6132  {Wifi,\"Air conditioning\",Kitchen,\"Free parking...                t   \n",
       "6133  {Wifi,\"Air conditioning\",Kitchen,Heating,Washe...                t   \n",
       "6134  {TV,Wifi,\"Air conditioning\",Pool,Kitchen,\"Free...                t   \n",
       "6135  {TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...                t   \n",
       "6136  {TV,Wifi,Kitchen,\"Free parking on premises\",He...                t   \n",
       "6137  {TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...                t   \n",
       "6138  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...                t   \n",
       "6139  {Wifi,\"Air conditioning\",Heating,Washer,Dryer,...                f   \n",
       "6140  {Wifi,\"Air conditioning\",\"Free parking on prem...                t   \n",
       "6141  {TV,\"Air conditioning\",Kitchen,Elevator,\"Indoo...                t   \n",
       "6142  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...                t   \n",
       "6143  {\"Air conditioning\",Kitchen,\"Pets allowed\",Hea...                t   \n",
       "6144  {TV,Kitchen,\"Pets allowed\",Heating,Washer,Drye...                t   \n",
       "6145  {Wifi,Kitchen,Heating,\"Family/kid friendly\",Wa...                t   \n",
       "6146  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...                f   \n",
       "6147  {Wifi,\"Air conditioning\",Kitchen,Breakfast,Hea...                t   \n",
       "6148  {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...                t   \n",
       "6149  {TV,Wifi,\"Air conditioning\",Pool,Kitchen,Eleva...                t   \n",
       "6150  {\"Air conditioning\",Kitchen,\"Pets allowed\",Bre...                t   \n",
       "6151  {TV,Wifi,Kitchen,\"Pets allowed\",Breakfast,Heat...                t   \n",
       "6152  {TV,Wifi,Kitchen,Breakfast,Heating,\"Smoke dete...                f   \n",
       "6153  {Wifi,Kitchen,\"Paid parking off premises\",Heat...                t   \n",
       "6154  {Wifi,Kitchen,\"Paid parking off premises\",Heat...                t   \n",
       "\n",
       "              cancellation_policy  \n",
       "0                 super_strict_30  \n",
       "1     strict_14_with_grace_period  \n",
       "2     strict_14_with_grace_period  \n",
       "3                        moderate  \n",
       "4     strict_14_with_grace_period  \n",
       "5     strict_14_with_grace_period  \n",
       "6                 super_strict_30  \n",
       "7                 super_strict_30  \n",
       "8                 super_strict_30  \n",
       "9                 super_strict_30  \n",
       "10                super_strict_30  \n",
       "11                super_strict_30  \n",
       "12                super_strict_30  \n",
       "13                super_strict_30  \n",
       "14                super_strict_30  \n",
       "15    strict_14_with_grace_period  \n",
       "16    strict_14_with_grace_period  \n",
       "17    strict_14_with_grace_period  \n",
       "18    strict_14_with_grace_period  \n",
       "19                super_strict_30  \n",
       "20                super_strict_30  \n",
       "21    strict_14_with_grace_period  \n",
       "22                super_strict_30  \n",
       "23                super_strict_30  \n",
       "24                       moderate  \n",
       "25                super_strict_30  \n",
       "26                super_strict_30  \n",
       "27    strict_14_with_grace_period  \n",
       "28                super_strict_30  \n",
       "29                super_strict_30  \n",
       "...                           ...  \n",
       "6125                     moderate  \n",
       "6126                     moderate  \n",
       "6127                     flexible  \n",
       "6128  strict_14_with_grace_period  \n",
       "6129  strict_14_with_grace_period  \n",
       "6130                     flexible  \n",
       "6131  strict_14_with_grace_period  \n",
       "6132  strict_14_with_grace_period  \n",
       "6133                     flexible  \n",
       "6134                     flexible  \n",
       "6135                     flexible  \n",
       "6136                     flexible  \n",
       "6137                     flexible  \n",
       "6138                     moderate  \n",
       "6139                     flexible  \n",
       "6140                     flexible  \n",
       "6141                     flexible  \n",
       "6142                     flexible  \n",
       "6143                     flexible  \n",
       "6144                     flexible  \n",
       "6145                     flexible  \n",
       "6146  strict_14_with_grace_period  \n",
       "6147                     flexible  \n",
       "6148  strict_14_with_grace_period  \n",
       "6149  strict_14_with_grace_period  \n",
       "6150                     flexible  \n",
       "6151                     flexible  \n",
       "6152  strict_14_with_grace_period  \n",
       "6153  strict_14_with_grace_period  \n",
       "6154  strict_14_with_grace_period  \n",
       "\n",
       "[6155 rows x 9 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the categorical features\n",
    "features_cat = df_listings_prep1.select_dtypes(include='object')\n",
    "features_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6155, 19)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "cancellation_policy         0\n",
       "instant_bookable            0\n",
       "number_of_reviews           0\n",
       "price                       0\n",
       "amenities                   0\n",
       "latitude                    0\n",
       "longitude                   0\n",
       "room_type                   0\n",
       "property_type               0\n",
       "zipcode                     0\n",
       "neighbourhood_cleansed      0\n",
       "accommodates                0\n",
       "host_is_superhost           2\n",
       "host_name                   2\n",
       "beds                        3\n",
       "bathrooms                   5\n",
       "bedrooms                    6\n",
       "cleaning_fee              979\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of nulls for each feature\n",
    "print(df_listings_prep1.shape)\n",
    "df_listings_prep1.isnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we are going to drop the listings where they have no values about **'host_name', 'beds', 'bathrooms', and 'bedrooms'**. The reason is that we cannot create a model when having missing values, thus, we clean the data as well as create dummy variables for the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6140, 19)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "cancellation_policy         0\n",
       "instant_bookable            0\n",
       "number_of_reviews           0\n",
       "price                       0\n",
       "amenities                   0\n",
       "beds                        0\n",
       "latitude                    0\n",
       "bedrooms                    0\n",
       "accommodates                0\n",
       "room_type                   0\n",
       "property_type               0\n",
       "zipcode                     0\n",
       "neighbourhood_cleansed      0\n",
       "host_is_superhost           0\n",
       "host_name                   0\n",
       "bathrooms                   0\n",
       "longitude                   0\n",
       "cleaning_fee              974\n",
       "dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping listings with missing values for host names, beds, bathrooms, and bedrooms\n",
    "df_listings_prep1 = df_listings_prep1.dropna(axis=0, subset=['host_name', 'beds', 'bathrooms', 'bedrooms'])\n",
    "print(df_listings_prep1.shape)\n",
    "df_listings_prep1.isnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With respect to the **cleaning_fee**, its missing values have been imputed because it could be used for a future analysis. Sometimes, people like to consider the **cleaning_fee** plus the rental price. Here, we will only foucs on the **price** variable. Again, instead of dropping the listings or the feature itself, it has been imputed to be added to our analysis if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        0\n",
       "host_name                 0\n",
       "host_is_superhost         0\n",
       "neighbourhood_cleansed    0\n",
       "zipcode                   0\n",
       "property_type             0\n",
       "room_type                 0\n",
       "accommodates              0\n",
       "bathrooms                 0\n",
       "bedrooms                  0\n",
       "beds                      0\n",
       "amenities                 0\n",
       "price                     0\n",
       "cleaning_fee              0\n",
       "number_of_reviews         0\n",
       "instant_bookable          0\n",
       "cancellation_policy       0\n",
       "latitude                  0\n",
       "longitude                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling nan values for cleaning_fee with the most frequent cleaning_fee\n",
    "df_listings_prep1 = df_listings_prep1.apply(lambda x:x.fillna(x.value_counts().index[0]))\n",
    "df_listings_prep1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#assessing the categorical features: which are binary, which are multi-level\n",
    "\n",
    "#binary\n",
    "binary_list=[]\n",
    "#multi-level\n",
    "multi_level_list=[]\n",
    "\n",
    "for f in features_cat:\n",
    "    if (len(df_listings_prep1[f].unique())==2):\n",
    "        binary_list.append(f)\n",
    "    elif (len(df_listings_prep1[f].unique())>2):\n",
    "        multi_level_list.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "host_is_superhost ['t' 'f']\n",
      "instant_bookable ['f' 't']\n"
     ]
    }
   ],
   "source": [
    "#listing the binary features with their unique values\n",
    "for b in binary_list:\n",
    "    print(b, df_listings_prep1[b].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#Re-encodeing the binary list\n",
    "df_listings_prep1['host_is_superhost'] = df_listings_prep1['host_is_superhost'].map({'t': 1, 'f': 0})\n",
    "df_listings_prep1['instant_bookable'] = df_listings_prep1['instant_bookable'].map({'t': 1, 'f': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "host_name ['Frank' 'Terry' 'Phil' ... 'Yaling' 'Kashyap' 'Gabi']\n",
      "neighbourhood_cleansed ['East Boston' 'Roxbury' 'Roslindale' 'Downtown' 'Fenway' 'Back Bay'\n",
      " 'South End' 'North End' 'Dorchester' 'West End' 'Jamaica Plain'\n",
      " 'Charlestown' 'Beacon Hill' 'Mission Hill' 'Allston' 'South Boston'\n",
      " 'Brighton' 'West Roxbury' 'Bay Village' 'South Boston Waterfront'\n",
      " 'Longwood Medical Area' 'Chinatown' 'Mattapan' 'Hyde Park'\n",
      " 'Leather District']\n",
      "zipcode ['02128' '02119' '02131' '02108' '02111' '02215' '02116' '02115' '02109'\n",
      " '02125' '02114' '02118' '02122' '02130' '02129' '02120' '02134' '02127'\n",
      " '02124' '02135' '02113' '02132' '02121' '02108 02111' '00nan' '02110'\n",
      " '02126' '02136' '02467' '02163' '02145' '02445' '02210' '33131' '02446'\n",
      " '02143' '02141' '02149' '01217' '02421' '02139' '02026']\n",
      "property_type ['Apartment' 'Guest suite' 'Condominium' 'Serviced apartment' 'Boat'\n",
      " 'House' 'Guesthouse' 'Bed and breakfast' 'Townhouse' 'Loft' 'Bungalow'\n",
      " 'Other' 'Villa' 'Boutique hotel' 'Resort' 'Hotel' 'Chalet' 'Tiny house'\n",
      " 'Aparthotel' 'Barn' 'Camper/RV']\n",
      "room_type ['Entire home/apt' 'Private room' 'Shared room']\n",
      "amenities ['{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen,\"Free parking on premises\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_49\",\"translation missing: en.hosting_amenity_50\",\"Hot water\",\"Bed linens\",\"Extra pillows and blankets\",Microwave,\"Coffee maker\",Refrigerator,Dishwasher,\"Dishes and silverware\",\"Cooking basics\",Oven,Stove}'\n",
      " '{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning\",\"Free street parking\",Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",\"Fire extinguisher\",Essentials,Shampoo,\"Lock on bedroom door\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Self check-in\",Keypad,\"Private entrance\",Bathtub,\"Hot water\",\"Bed linens\",\"Extra pillows and blankets\",Microwave,\"Coffee maker\",Refrigerator,\"Dishes and silverware\",\"Luggage dropoff allowed\",\"Long term stays allowed\"}'\n",
      " '{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning\",Kitchen,\"Free street parking\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Self check-in\",Keypad,\"Hot water\",\"Luggage dropoff allowed\",\"Long term stays allowed\"}'\n",
      " ...\n",
      " '{TV,Wifi,Kitchen,\"Pets allowed\",Breakfast,Heating,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,\"Lock on bedroom door\",Hangers,\"Laptop friendly workspace\"}'\n",
      " '{TV,Wifi,Kitchen,Breakfast,Heating,\"Smoke detector\",\"Carbon monoxide detector\",Essentials,Shampoo,\"Hair dryer\",Iron,\"Private entrance\"}'\n",
      " '{Wifi,Kitchen,\"Paid parking off premises\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",Essentials,Shampoo,\"Lock on bedroom door\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Self check-in\",Keypad,\"Hot water\",\"Bed linens\",\"Extra pillows and blankets\",Microwave,\"Coffee maker\",Refrigerator,Dishwasher,\"Dishes and silverware\",\"Cooking basics\",Oven,Stove}']\n",
      "cancellation_policy ['super_strict_30' 'strict_14_with_grace_period' 'moderate' 'flexible'\n",
      " 'super_strict_60' 'strict']\n"
     ]
    }
   ],
   "source": [
    "#listing the multilevel features with their unique values\n",
    "for m in multi_level_list:\n",
    "    print(m, df_listings_prep1[m].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>amenities</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3781</td>\n",
       "      <td>Frank</td>\n",
       "      <td>1</td>\n",
       "      <td>East Boston</td>\n",
       "      <td>02128</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.365241</td>\n",
       "      <td>-71.029361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5506</td>\n",
       "      <td>Terry</td>\n",
       "      <td>1</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Guest suite</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>145.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.329808</td>\n",
       "      <td>-71.095595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6695</td>\n",
       "      <td>Terry</td>\n",
       "      <td>1</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.329941</td>\n",
       "      <td>-71.093505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6976</td>\n",
       "      <td>Phil</td>\n",
       "      <td>1</td>\n",
       "      <td>Roslindale</td>\n",
       "      <td>02131</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>42.292438</td>\n",
       "      <td>-71.135765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8789</td>\n",
       "      <td>Anne</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.359187</td>\n",
       "      <td>-71.062651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8792</td>\n",
       "      <td>Anne</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.358497</td>\n",
       "      <td>-71.062011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9827</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02111</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>389.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.352149</td>\n",
       "      <td>-71.063301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9855</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>02215</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.343371</td>\n",
       "      <td>-71.098708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9857</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>342.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.354290</td>\n",
       "      <td>-71.072772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9858</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>489.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.344471</td>\n",
       "      <td>-71.081786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9859</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.350260</td>\n",
       "      <td>-71.084718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9860</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.354115</td>\n",
       "      <td>-71.074414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9870</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>South End</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>279.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.344227</td>\n",
       "      <td>-71.077878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10730</td>\n",
       "      <td>Anne</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02108</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.358403</td>\n",
       "      <td>-71.061850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10811</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.349730</td>\n",
       "      <td>-71.087924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10813</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02115</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.349613</td>\n",
       "      <td>-71.089038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10986</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>0</td>\n",
       "      <td>North End</td>\n",
       "      <td>02109</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.363520</td>\n",
       "      <td>-71.050754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13589</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>389.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.355263</td>\n",
       "      <td>-71.072793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13592</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>359.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.353763</td>\n",
       "      <td>-71.074239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18711</td>\n",
       "      <td>Lance</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02125</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>126.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.323286</td>\n",
       "      <td>-71.062103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21891</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02111</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>289.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.352179</td>\n",
       "      <td>-71.063160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23370</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>289.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.346091</td>\n",
       "      <td>-71.078578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23668</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>249.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.363865</td>\n",
       "      <td>-71.068950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>24240</td>\n",
       "      <td>Gretchen</td>\n",
       "      <td>1</td>\n",
       "      <td>North End</td>\n",
       "      <td>02109</td>\n",
       "      <td>Boat</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,Internet,Wifi,Kitchen,\"Paid parking off pr...</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.363660</td>\n",
       "      <td>-71.050232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>25418</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...</td>\n",
       "      <td>279.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.364945</td>\n",
       "      <td>-71.067668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>27141</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>South End</td>\n",
       "      <td>02118</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>279.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.342586</td>\n",
       "      <td>-71.079691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>27546</td>\n",
       "      <td>Hermina</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02122</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{\"Cable TV\",Internet,Wifi,Kitchen,Heating,\"Fam...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.298349</td>\n",
       "      <td>-71.059104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>27611</td>\n",
       "      <td>Gordon And Alexandra</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,Kitchen,\"Free street parking\",Heating,\"S...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.315565</td>\n",
       "      <td>-71.114827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28150</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>184.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.353975</td>\n",
       "      <td>-71.072612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>29155</td>\n",
       "      <td>Seamless</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>451.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>super_strict_30</td>\n",
       "      <td>42.354328</td>\n",
       "      <td>-71.073521</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6125</th>\n",
       "      <td>32116807</td>\n",
       "      <td>Cortney</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "      <td>42.321519</td>\n",
       "      <td>-71.110608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6126</th>\n",
       "      <td>32125673</td>\n",
       "      <td>Shekeyla</td>\n",
       "      <td>0</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Free parking on premises\",He...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "      <td>42.362714</td>\n",
       "      <td>-71.131194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>32127951</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>0</td>\n",
       "      <td>East Boston</td>\n",
       "      <td>02128</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.373256</td>\n",
       "      <td>-71.032211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6128</th>\n",
       "      <td>32133411</td>\n",
       "      <td>Wendy</td>\n",
       "      <td>0</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Pets allo...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.326400</td>\n",
       "      <td>-71.092240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>32133771</td>\n",
       "      <td>Paige</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02109</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Paid p...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.359055</td>\n",
       "      <td>-71.052996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>32138377</td>\n",
       "      <td>Rachel</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02135</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.354935</td>\n",
       "      <td>-71.165600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6131</th>\n",
       "      <td>32141341</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",\"Pets allowed\",\"Free ...</td>\n",
       "      <td>200.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.325101</td>\n",
       "      <td>-71.105237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6132</th>\n",
       "      <td>32147828</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,\"Free parking...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.365379</td>\n",
       "      <td>-71.069163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6133</th>\n",
       "      <td>32155120</td>\n",
       "      <td>Taryn</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02122</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Heating,Washe...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.288390</td>\n",
       "      <td>-71.050601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6134</th>\n",
       "      <td>32158419</td>\n",
       "      <td>Hesham</td>\n",
       "      <td>0</td>\n",
       "      <td>Mission Hill</td>\n",
       "      <td>02130</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Pool,Kitchen,\"Free...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.331076</td>\n",
       "      <td>-71.110691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6135</th>\n",
       "      <td>32161143</td>\n",
       "      <td>Ekaterina</td>\n",
       "      <td>0</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>02114</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.360359</td>\n",
       "      <td>-71.066792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6136</th>\n",
       "      <td>32162031</td>\n",
       "      <td>Mojisola</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02124</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Free parking on premises\",He...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.287114</td>\n",
       "      <td>-71.092115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6137</th>\n",
       "      <td>32163609</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "      <td>02114</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...</td>\n",
       "      <td>109.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.361173</td>\n",
       "      <td>-71.070311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6138</th>\n",
       "      <td>32163985</td>\n",
       "      <td>Stayhere</td>\n",
       "      <td>0</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>02119</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...</td>\n",
       "      <td>199.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "      <td>42.327272</td>\n",
       "      <td>-71.080344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6139</th>\n",
       "      <td>32168098</td>\n",
       "      <td>David</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>02130</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Heating,Washer,Dryer,...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.319170</td>\n",
       "      <td>-71.105199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6140</th>\n",
       "      <td>32172713</td>\n",
       "      <td>Brigith</td>\n",
       "      <td>0</td>\n",
       "      <td>Mission Hill</td>\n",
       "      <td>02120</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",\"Free parking on prem...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.330545</td>\n",
       "      <td>-71.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141</th>\n",
       "      <td>32180329</td>\n",
       "      <td>Lola</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Air conditioning\",Kitchen,Elevator,\"Indoo...</td>\n",
       "      <td>220.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.345382</td>\n",
       "      <td>-71.079692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6142</th>\n",
       "      <td>32183321</td>\n",
       "      <td>Hank</td>\n",
       "      <td>0</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.350709</td>\n",
       "      <td>-71.134464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6143</th>\n",
       "      <td>32188679</td>\n",
       "      <td>Yaling</td>\n",
       "      <td>0</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02134</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{\"Air conditioning\",Kitchen,\"Pets allowed\",Hea...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.352673</td>\n",
       "      <td>-71.129030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>32195225</td>\n",
       "      <td>Dmitriy</td>\n",
       "      <td>0</td>\n",
       "      <td>South Boston</td>\n",
       "      <td>02127</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Kitchen,\"Pets allowed\",Heating,Washer,Drye...</td>\n",
       "      <td>200.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.332255</td>\n",
       "      <td>-71.030875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6145</th>\n",
       "      <td>32196121</td>\n",
       "      <td>Kashyap</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02135</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,Kitchen,Heating,\"Family/kid friendly\",Wa...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.346257</td>\n",
       "      <td>-71.140473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>32197096</td>\n",
       "      <td>Alan</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02121</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.300713</td>\n",
       "      <td>-71.084060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6147</th>\n",
       "      <td>32202426</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>0</td>\n",
       "      <td>Allston</td>\n",
       "      <td>02139</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{Wifi,\"Air conditioning\",Kitchen,Breakfast,Hea...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.355790</td>\n",
       "      <td>-71.110322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6148</th>\n",
       "      <td>32213220</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>0</td>\n",
       "      <td>Fenway</td>\n",
       "      <td>02215</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.346640</td>\n",
       "      <td>-71.103684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>32223560</td>\n",
       "      <td>Jane</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02109</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Pool,Kitchen,Eleva...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.359254</td>\n",
       "      <td>-71.053765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6150</th>\n",
       "      <td>32234660</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>0</td>\n",
       "      <td>West Roxbury</td>\n",
       "      <td>02132</td>\n",
       "      <td>Camper/RV</td>\n",
       "      <td>Private room</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>{\"Air conditioning\",Kitchen,\"Pets allowed\",Bre...</td>\n",
       "      <td>125.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.262505</td>\n",
       "      <td>-71.151490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>32238109</td>\n",
       "      <td>Courtney</td>\n",
       "      <td>0</td>\n",
       "      <td>Dorchester</td>\n",
       "      <td>02125</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Pets allowed\",Breakfast,Heat...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>flexible</td>\n",
       "      <td>42.318001</td>\n",
       "      <td>-71.053537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>32241248</td>\n",
       "      <td>Gabi</td>\n",
       "      <td>0</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,Kitchen,Breakfast,Heating,\"Smoke dete...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.349531</td>\n",
       "      <td>-71.082424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>32242842</td>\n",
       "      <td>Will</td>\n",
       "      <td>0</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>02116</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,Kitchen,\"Paid parking off premises\",Heat...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.351701</td>\n",
       "      <td>-71.065359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>32243378</td>\n",
       "      <td>Hilal</td>\n",
       "      <td>1</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>02446</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{Wifi,Kitchen,\"Paid parking off premises\",Heat...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>42.340967</td>\n",
       "      <td>-71.134066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6140 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id             host_name  host_is_superhost  \\\n",
       "0         3781                 Frank                  1   \n",
       "1         5506                 Terry                  1   \n",
       "2         6695                 Terry                  1   \n",
       "3         6976                  Phil                  1   \n",
       "4         8789                  Anne                  0   \n",
       "5         8792                  Anne                  0   \n",
       "8         9827              Seamless                  0   \n",
       "9         9855              Seamless                  0   \n",
       "10        9857              Seamless                  0   \n",
       "11        9858              Seamless                  0   \n",
       "12        9859              Seamless                  0   \n",
       "13        9860              Seamless                  0   \n",
       "14        9870              Seamless                  0   \n",
       "15       10730                  Anne                  0   \n",
       "16       10811              Michelle                  0   \n",
       "17       10813              Michelle                  0   \n",
       "18       10986              Michelle                  0   \n",
       "19       13589              Seamless                  0   \n",
       "20       13592              Seamless                  0   \n",
       "21       18711                 Lance                  0   \n",
       "22       21891              Seamless                  0   \n",
       "25       23370              Seamless                  0   \n",
       "26       23668              Seamless                  0   \n",
       "27       24240              Gretchen                  1   \n",
       "29       25418              Seamless                  0   \n",
       "30       27141              Seamless                  0   \n",
       "31       27546               Hermina                  0   \n",
       "32       27611  Gordon And Alexandra                  0   \n",
       "33       28150              Seamless                  0   \n",
       "34       29155              Seamless                  0   \n",
       "...        ...                   ...                ...   \n",
       "6125  32116807               Cortney                  0   \n",
       "6126  32125673              Shekeyla                  0   \n",
       "6127  32127951                Taylor                  0   \n",
       "6128  32133411                 Wendy                  0   \n",
       "6129  32133771                 Paige                  0   \n",
       "6130  32138377                Rachel                  0   \n",
       "6131  32141341                Nicole                  0   \n",
       "6132  32147828                Thomas                  0   \n",
       "6133  32155120                 Taryn                  0   \n",
       "6134  32158419                Hesham                  0   \n",
       "6135  32161143             Ekaterina                  0   \n",
       "6136  32162031              Mojisola                  0   \n",
       "6137  32163609                Ashley                  0   \n",
       "6138  32163985              Stayhere                  0   \n",
       "6139  32168098                 David                  0   \n",
       "6140  32172713               Brigith                  0   \n",
       "6141  32180329                  Lola                  0   \n",
       "6142  32183321                  Hank                  0   \n",
       "6143  32188679                Yaling                  0   \n",
       "6144  32195225               Dmitriy                  0   \n",
       "6145  32196121               Kashyap                  0   \n",
       "6146  32197096                  Alan                  0   \n",
       "6147  32202426               Barbara                  0   \n",
       "6148  32213220                Taylor                  0   \n",
       "6149  32223560                  Jane                  0   \n",
       "6150  32234660                Daniel                  0   \n",
       "6151  32238109              Courtney                  0   \n",
       "6152  32241248                  Gabi                  0   \n",
       "6153  32242842                  Will                  0   \n",
       "6154  32243378                 Hilal                  1   \n",
       "\n",
       "     neighbourhood_cleansed zipcode       property_type        room_type  \\\n",
       "0               East Boston   02128           Apartment  Entire home/apt   \n",
       "1                   Roxbury   02119         Guest suite  Entire home/apt   \n",
       "2                   Roxbury   02119         Condominium  Entire home/apt   \n",
       "3                Roslindale   02131           Apartment     Private room   \n",
       "4                  Downtown   02108           Apartment  Entire home/apt   \n",
       "5                  Downtown   02108           Apartment  Entire home/apt   \n",
       "8                  Downtown   02111  Serviced apartment  Entire home/apt   \n",
       "9                    Fenway   02215           Apartment  Entire home/apt   \n",
       "10                 Back Bay   02116           Apartment  Entire home/apt   \n",
       "11                 Back Bay   02116           Apartment  Entire home/apt   \n",
       "12                 Back Bay   02115  Serviced apartment  Entire home/apt   \n",
       "13                 Back Bay   02116  Serviced apartment  Entire home/apt   \n",
       "14                South End   02116  Serviced apartment  Entire home/apt   \n",
       "15                 Downtown   02108           Apartment  Entire home/apt   \n",
       "16                 Back Bay   02115           Apartment  Entire home/apt   \n",
       "17                 Back Bay   02115           Apartment  Entire home/apt   \n",
       "18                North End   02109         Condominium  Entire home/apt   \n",
       "19                 Back Bay   02116  Serviced apartment  Entire home/apt   \n",
       "20                 Back Bay   02116           Apartment  Entire home/apt   \n",
       "21               Dorchester   02125  Serviced apartment  Entire home/apt   \n",
       "22                 Downtown   02111           Apartment  Entire home/apt   \n",
       "25                 Back Bay   02116           Apartment  Entire home/apt   \n",
       "26                 West End   02114  Serviced apartment  Entire home/apt   \n",
       "27                North End   02109                Boat  Entire home/apt   \n",
       "29                 West End   02114           Apartment  Entire home/apt   \n",
       "30                South End   02118  Serviced apartment  Entire home/apt   \n",
       "31               Dorchester   02122               House     Private room   \n",
       "32            Jamaica Plain   02130           Apartment  Entire home/apt   \n",
       "33                 Back Bay   02116           Apartment  Entire home/apt   \n",
       "34                 Back Bay   02116  Serviced apartment  Entire home/apt   \n",
       "...                     ...     ...                 ...              ...   \n",
       "6125          Jamaica Plain   02130           Apartment     Private room   \n",
       "6126                Allston   02134           Apartment     Private room   \n",
       "6127            East Boston   02128           Apartment  Entire home/apt   \n",
       "6128                Roxbury   02119               House     Private room   \n",
       "6129               Downtown   02109           Apartment  Entire home/apt   \n",
       "6130               Brighton   02135               House     Private room   \n",
       "6131          Jamaica Plain   02130               House     Private room   \n",
       "6132               West End   02114           Apartment     Private room   \n",
       "6133             Dorchester   02122           Apartment     Private room   \n",
       "6134           Mission Hill   02130           Apartment  Entire home/apt   \n",
       "6135            Beacon Hill   02114         Condominium  Entire home/apt   \n",
       "6136             Dorchester   02124           Apartment     Private room   \n",
       "6137               West End   02114           Apartment  Entire home/apt   \n",
       "6138                Roxbury   02119           Apartment  Entire home/apt   \n",
       "6139          Jamaica Plain   02130         Condominium     Private room   \n",
       "6140           Mission Hill   02120               House     Private room   \n",
       "6141               Back Bay   02116           Apartment  Entire home/apt   \n",
       "6142                Allston   02134               House     Private room   \n",
       "6143                Allston   02134           Apartment     Private room   \n",
       "6144           South Boston   02127           Apartment  Entire home/apt   \n",
       "6145               Brighton   02135           Apartment     Private room   \n",
       "6146             Dorchester   02121         Condominium  Entire home/apt   \n",
       "6147                Allston   02139               House     Private room   \n",
       "6148                 Fenway   02215           Apartment  Entire home/apt   \n",
       "6149               Downtown   02109         Condominium  Entire home/apt   \n",
       "6150           West Roxbury   02132           Camper/RV     Private room   \n",
       "6151             Dorchester   02125               House     Private room   \n",
       "6152               Back Bay   02116           Apartment  Entire home/apt   \n",
       "6153               Downtown   02116           Apartment     Private room   \n",
       "6154               Brighton   02446           Apartment     Private room   \n",
       "\n",
       "      accommodates  bathrooms  bedrooms  beds  \\\n",
       "0              2.0        1.0       1.0   1.0   \n",
       "1              2.0        1.0       1.0   1.0   \n",
       "2              4.0        1.0       1.0   2.0   \n",
       "3              2.0        1.0       1.0   1.0   \n",
       "4              2.0        1.0       1.0   1.0   \n",
       "5              2.0        1.0       1.0   1.0   \n",
       "8              2.0        1.0       1.0   1.0   \n",
       "9              3.0        1.0       1.0   1.0   \n",
       "10             4.0        1.0       2.0   2.0   \n",
       "11             6.0        1.0       2.0   2.0   \n",
       "12             2.0        1.0       1.0   1.0   \n",
       "13             2.0        1.0       1.0   1.0   \n",
       "14             2.0        1.0       1.0   1.0   \n",
       "15             2.0        1.0       1.0   1.0   \n",
       "16             3.0        1.0       0.0   2.0   \n",
       "17             3.0        1.0       0.0   1.0   \n",
       "18             2.0        1.0       0.0   1.0   \n",
       "19             6.0        2.0       3.0   3.0   \n",
       "20             4.0        1.5       2.0   2.0   \n",
       "21             7.0        1.0       3.0   3.0   \n",
       "22             4.0        1.0       2.0   2.0   \n",
       "25             2.0        1.0       1.0   1.0   \n",
       "26             2.0        1.0       1.0   1.0   \n",
       "27             5.0        2.0       2.0   3.0   \n",
       "29             2.0        1.0       1.0   1.0   \n",
       "30             2.0        1.0       1.0   1.0   \n",
       "31             4.0        1.0       1.0   2.0   \n",
       "32             2.0        1.0       1.0   1.0   \n",
       "33             2.0        1.0       1.0   1.0   \n",
       "34             6.0        1.5       3.0   3.0   \n",
       "...            ...        ...       ...   ...   \n",
       "6125           2.0        1.0       1.0   1.0   \n",
       "6126           2.0        1.0       1.0   1.0   \n",
       "6127           5.0        1.0       2.0   2.0   \n",
       "6128           3.0        1.5       1.0   2.0   \n",
       "6129           2.0        1.0       0.0   1.0   \n",
       "6130           4.0        1.5       3.0   3.0   \n",
       "6131           5.0        2.0       1.0   4.0   \n",
       "6132           2.0        1.0       1.0   1.0   \n",
       "6133           2.0        1.0       1.0   1.0   \n",
       "6134           4.0        1.0       2.0   3.0   \n",
       "6135           3.0        1.0       2.0   2.0   \n",
       "6136           2.0        1.0       1.0   1.0   \n",
       "6137           3.0        1.0       1.0   1.0   \n",
       "6138           4.0        1.0       2.0   2.0   \n",
       "6139           2.0        1.0       1.0   1.0   \n",
       "6140           1.0        1.0       1.0   1.0   \n",
       "6141           5.0        1.0       2.0   1.0   \n",
       "6142           2.0        1.0       1.0   1.0   \n",
       "6143           1.0        1.0       0.0   1.0   \n",
       "6144           4.0        1.5       2.0   1.0   \n",
       "6145           2.0        1.0       1.0   1.0   \n",
       "6146           4.0        1.0       1.0   2.0   \n",
       "6147           2.0        1.0       1.0   2.0   \n",
       "6148           3.0        1.0       0.0   2.0   \n",
       "6149           4.0        1.0       1.0   1.0   \n",
       "6150           6.0        1.0       1.0   3.0   \n",
       "6151           2.0        1.0       1.0   1.0   \n",
       "6152           4.0        1.0       1.0   2.0   \n",
       "6153           2.0        1.0       1.0   1.0   \n",
       "6154           3.0        1.0       1.0   1.0   \n",
       "\n",
       "                                              amenities  price  cleaning_fee  \\\n",
       "0     {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  125.0          75.0   \n",
       "1     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  145.0          40.0   \n",
       "2     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  169.0          70.0   \n",
       "3     {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...   65.0           0.0   \n",
       "4     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...   99.0         250.0   \n",
       "5     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  154.0         250.0   \n",
       "8     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  389.0         150.0   \n",
       "9     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  259.0         150.0   \n",
       "10    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  342.0         100.0   \n",
       "11    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  489.0         100.0   \n",
       "12    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  179.0         100.0   \n",
       "13    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  259.0         100.0   \n",
       "14    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  279.0         100.0   \n",
       "15    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  150.0         250.0   \n",
       "16    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  179.0         100.0   \n",
       "17    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  179.0         100.0   \n",
       "18    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  115.0         145.0   \n",
       "19    {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  389.0         100.0   \n",
       "20    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  359.0         100.0   \n",
       "21    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  126.0         120.0   \n",
       "22    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  289.0         200.0   \n",
       "25    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  289.0         150.0   \n",
       "26    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  249.0         150.0   \n",
       "27    {TV,Internet,Wifi,Kitchen,\"Paid parking off pr...  275.0           0.0   \n",
       "29    {TV,\"Cable TV\",Internet,\"Air conditioning\",\"Wh...  279.0         200.0   \n",
       "30    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  279.0         100.0   \n",
       "31    {\"Cable TV\",Internet,Wifi,Kitchen,Heating,\"Fam...   55.0          25.0   \n",
       "32    {Wifi,Kitchen,\"Free street parking\",Heating,\"S...  175.0          60.0   \n",
       "33    {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  184.0         100.0   \n",
       "34    {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  451.0         100.0   \n",
       "...                                                 ...    ...           ...   \n",
       "6125  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  120.0          35.0   \n",
       "6126  {TV,Wifi,Kitchen,\"Free parking on premises\",He...   60.0          15.0   \n",
       "6127  {Wifi,\"Air conditioning\",Kitchen,Heating,\"Smok...  143.0          40.0   \n",
       "6128  {TV,Wifi,\"Air conditioning\",Kitchen,\"Pets allo...   70.0          25.0   \n",
       "6129  {TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Paid p...  100.0          60.0   \n",
       "6130  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...   65.0          20.0   \n",
       "6131  {Wifi,\"Air conditioning\",\"Pets allowed\",\"Free ...  200.0          30.0   \n",
       "6132  {Wifi,\"Air conditioning\",Kitchen,\"Free parking...  125.0          40.0   \n",
       "6133  {Wifi,\"Air conditioning\",Kitchen,Heating,Washe...   43.0         100.0   \n",
       "6134  {TV,Wifi,\"Air conditioning\",Pool,Kitchen,\"Free...  160.0         100.0   \n",
       "6135  {TV,Wifi,Kitchen,Heating,Washer,Dryer,\"Smoke d...  160.0         100.0   \n",
       "6136  {TV,Wifi,Kitchen,\"Free parking on premises\",He...   45.0           0.0   \n",
       "6137  {TV,Wifi,\"Air conditioning\",Kitchen,Heating,\"S...  109.0          30.0   \n",
       "6138  {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...  199.0          25.0   \n",
       "6139  {Wifi,\"Air conditioning\",Heating,Washer,Dryer,...   85.0         100.0   \n",
       "6140  {Wifi,\"Air conditioning\",\"Free parking on prem...   60.0         100.0   \n",
       "6141  {TV,\"Air conditioning\",Kitchen,Elevator,\"Indoo...  220.0         100.0   \n",
       "6142  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...   59.0          25.0   \n",
       "6143  {\"Air conditioning\",Kitchen,\"Pets allowed\",Hea...   60.0         100.0   \n",
       "6144  {TV,Kitchen,\"Pets allowed\",Heating,Washer,Drye...  200.0         100.0   \n",
       "6145  {Wifi,Kitchen,Heating,\"Family/kid friendly\",Wa...   45.0         100.0   \n",
       "6146  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free park...   75.0         100.0   \n",
       "6147  {Wifi,\"Air conditioning\",Kitchen,Breakfast,Hea...   80.0         100.0   \n",
       "6148  {TV,\"Cable TV\",Internet,Wifi,Kitchen,\"Pets all...  110.0         100.0   \n",
       "6149  {TV,Wifi,\"Air conditioning\",Pool,Kitchen,Eleva...  250.0         100.0   \n",
       "6150  {\"Air conditioning\",Kitchen,\"Pets allowed\",Bre...  125.0         100.0   \n",
       "6151  {TV,Wifi,Kitchen,\"Pets allowed\",Breakfast,Heat...   75.0         100.0   \n",
       "6152  {TV,Wifi,Kitchen,Breakfast,Heating,\"Smoke dete...   80.0          25.0   \n",
       "6153  {Wifi,Kitchen,\"Paid parking off premises\",Heat...   60.0          40.0   \n",
       "6154  {Wifi,Kitchen,\"Paid parking off premises\",Heat...   66.0           8.0   \n",
       "\n",
       "      number_of_reviews  instant_bookable          cancellation_policy  \\\n",
       "0                  14.0                 0              super_strict_30   \n",
       "1                  80.0                 1  strict_14_with_grace_period   \n",
       "2                  85.0                 1  strict_14_with_grace_period   \n",
       "3                  75.0                 0                     moderate   \n",
       "4                  22.0                 0  strict_14_with_grace_period   \n",
       "5                  24.0                 0  strict_14_with_grace_period   \n",
       "8                   8.0                 0              super_strict_30   \n",
       "9                   3.0                 0              super_strict_30   \n",
       "10                 25.0                 0              super_strict_30   \n",
       "11                  1.0                 0              super_strict_30   \n",
       "12                  3.0                 0              super_strict_30   \n",
       "13                 22.0                 0              super_strict_30   \n",
       "14                  8.0                 0              super_strict_30   \n",
       "15                 27.0                 0  strict_14_with_grace_period   \n",
       "16                  7.0                 0  strict_14_with_grace_period   \n",
       "17                  1.0                 0  strict_14_with_grace_period   \n",
       "18                  2.0                 0  strict_14_with_grace_period   \n",
       "19                 63.0                 0              super_strict_30   \n",
       "20                 20.0                 0              super_strict_30   \n",
       "21                 43.0                 1  strict_14_with_grace_period   \n",
       "22                 21.0                 0              super_strict_30   \n",
       "25                  5.0                 0              super_strict_30   \n",
       "26                  0.0                 0              super_strict_30   \n",
       "27                 82.0                 0  strict_14_with_grace_period   \n",
       "29                  4.0                 0              super_strict_30   \n",
       "30                  4.0                 0              super_strict_30   \n",
       "31                234.0                 1                     flexible   \n",
       "32                 15.0                 1  strict_14_with_grace_period   \n",
       "33                 21.0                 0              super_strict_30   \n",
       "34                 33.0                 0              super_strict_30   \n",
       "...                 ...               ...                          ...   \n",
       "6125                0.0                 1                     moderate   \n",
       "6126                0.0                 1                     moderate   \n",
       "6127                0.0                 1                     flexible   \n",
       "6128                0.0                 1  strict_14_with_grace_period   \n",
       "6129                0.0                 1  strict_14_with_grace_period   \n",
       "6130                0.0                 1                     flexible   \n",
       "6131                0.0                 1  strict_14_with_grace_period   \n",
       "6132                0.0                 1  strict_14_with_grace_period   \n",
       "6133                0.0                 1                     flexible   \n",
       "6134                0.0                 1                     flexible   \n",
       "6135                0.0                 1                     flexible   \n",
       "6136                0.0                 1                     flexible   \n",
       "6137                1.0                 1                     flexible   \n",
       "6138                0.0                 1                     moderate   \n",
       "6139                0.0                 0                     flexible   \n",
       "6140                0.0                 1                     flexible   \n",
       "6141                0.0                 1                     flexible   \n",
       "6142                0.0                 1                     flexible   \n",
       "6143                0.0                 1                     flexible   \n",
       "6144                0.0                 1                     flexible   \n",
       "6145                0.0                 1                     flexible   \n",
       "6146                0.0                 0  strict_14_with_grace_period   \n",
       "6147                0.0                 1                     flexible   \n",
       "6148                0.0                 1  strict_14_with_grace_period   \n",
       "6149                0.0                 1  strict_14_with_grace_period   \n",
       "6150                0.0                 1                     flexible   \n",
       "6151                0.0                 1                     flexible   \n",
       "6152                0.0                 0  strict_14_with_grace_period   \n",
       "6153                0.0                 1  strict_14_with_grace_period   \n",
       "6154                0.0                 1  strict_14_with_grace_period   \n",
       "\n",
       "       latitude  longitude  \n",
       "0     42.365241 -71.029361  \n",
       "1     42.329808 -71.095595  \n",
       "2     42.329941 -71.093505  \n",
       "3     42.292438 -71.135765  \n",
       "4     42.359187 -71.062651  \n",
       "5     42.358497 -71.062011  \n",
       "8     42.352149 -71.063301  \n",
       "9     42.343371 -71.098708  \n",
       "10    42.354290 -71.072772  \n",
       "11    42.344471 -71.081786  \n",
       "12    42.350260 -71.084718  \n",
       "13    42.354115 -71.074414  \n",
       "14    42.344227 -71.077878  \n",
       "15    42.358403 -71.061850  \n",
       "16    42.349730 -71.087924  \n",
       "17    42.349613 -71.089038  \n",
       "18    42.363520 -71.050754  \n",
       "19    42.355263 -71.072793  \n",
       "20    42.353763 -71.074239  \n",
       "21    42.323286 -71.062103  \n",
       "22    42.352179 -71.063160  \n",
       "25    42.346091 -71.078578  \n",
       "26    42.363865 -71.068950  \n",
       "27    42.363660 -71.050232  \n",
       "29    42.364945 -71.067668  \n",
       "30    42.342586 -71.079691  \n",
       "31    42.298349 -71.059104  \n",
       "32    42.315565 -71.114827  \n",
       "33    42.353975 -71.072612  \n",
       "34    42.354328 -71.073521  \n",
       "...         ...        ...  \n",
       "6125  42.321519 -71.110608  \n",
       "6126  42.362714 -71.131194  \n",
       "6127  42.373256 -71.032211  \n",
       "6128  42.326400 -71.092240  \n",
       "6129  42.359055 -71.052996  \n",
       "6130  42.354935 -71.165600  \n",
       "6131  42.325101 -71.105237  \n",
       "6132  42.365379 -71.069163  \n",
       "6133  42.288390 -71.050601  \n",
       "6134  42.331076 -71.110691  \n",
       "6135  42.360359 -71.066792  \n",
       "6136  42.287114 -71.092115  \n",
       "6137  42.361173 -71.070311  \n",
       "6138  42.327272 -71.080344  \n",
       "6139  42.319170 -71.105199  \n",
       "6140  42.330545 -71.101546  \n",
       "6141  42.345382 -71.079692  \n",
       "6142  42.350709 -71.134464  \n",
       "6143  42.352673 -71.129030  \n",
       "6144  42.332255 -71.030875  \n",
       "6145  42.346257 -71.140473  \n",
       "6146  42.300713 -71.084060  \n",
       "6147  42.355790 -71.110322  \n",
       "6148  42.346640 -71.103684  \n",
       "6149  42.359254 -71.053765  \n",
       "6150  42.262505 -71.151490  \n",
       "6151  42.318001 -71.053537  \n",
       "6152  42.349531 -71.082424  \n",
       "6153  42.351701 -71.065359  \n",
       "6154  42.340967 -71.134066  \n",
       "\n",
       "[6140 rows x 19 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#viewing the dataset after re-encoding the binary list\n",
    "df_listings_prep1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q3. Who are the hosts with the most number of airbnb listings? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "host_name\n",
       "Sonder                306\n",
       "Kara                  155\n",
       "Bluebird              153\n",
       "Mike                  142\n",
       "Brent                  89\n",
       "Stay Alfred            85\n",
       "Jen                    80\n",
       "Corp Condos & Apts     70\n",
       "Sonder (Boston)        58\n",
       "Maverick               56\n",
       "Marie                  54\n",
       "Matthew                49\n",
       "Will                   48\n",
       "Domio                  44\n",
       "Taylor                 43\n",
       "Alex                   41\n",
       "Paige                  40\n",
       "Blueground             39\n",
       "Huggy                  36\n",
       "Inn Boston             36\n",
       "Michelle               34\n",
       "Michael                33\n",
       "Ken                    33\n",
       "David                  32\n",
       "Mario                  31\n",
       "Chris                  30\n",
       "Jason                  29\n",
       "Luxurious              28\n",
       "Seamless               28\n",
       "Jonathan               27\n",
       "Jennifer               26\n",
       "Kevin                  26\n",
       "Robert                 25\n",
       "Susan                  24\n",
       "Anne                   24\n",
       "Kiki                   24\n",
       "Lance                  23\n",
       "Nav                    22\n",
       "Adam                   22\n",
       "John                   22\n",
       "Sarah                  21\n",
       "Stephanie              20\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hosts_count = df_listings_prep1.groupby('host_name').count()['id'].sort_values(ascending=False)\n",
    "hosts_count[hosts_count >= 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/476.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_count = df_listings_prep1.groupby('host_name').count()['id']\n",
    "h_count = h_count[h_count >= 10]\n",
    "h_count = h_count.sort_values(ascending=False)\n",
    "x = h_count.index\n",
    "y = h_count\n",
    "y_cum = np.cumsum(y)\n",
    "y_perc = 100*y_cum/y.sum()\n",
    "\n",
    "trace1 = dict(type='bar',\n",
    "    x=x,\n",
    "    y=y,\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5)),\n",
    "    name='Count of Listings',\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "trace2 = dict(type='scatter',\n",
    "    x=x,\n",
    "    y=y_perc,\n",
    "    marker=dict(\n",
    "        color='#7F7F7F'\n",
    "    ),\n",
    "    line=dict(color= '#7F7F7F', width= 1.5),\n",
    "    name='Cumulative Listings Percent of Total (%)',\n",
    "    xaxis='x1', \n",
    "    yaxis='y2' \n",
    ")\n",
    "    \n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(\n",
    "    title='Number of Listings by Host',\n",
    "    legend= dict(x=-.1, y=1.2),\n",
    "    yaxis=dict(\n",
    "        title='Count of Listings'\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        title='Percent of Total Listings (%)',\n",
    "        range=[0,100],\n",
    "        overlaying='y',\n",
    "        anchor='x',\n",
    "        side='right'\n",
    "        )\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Percent of listings by Host')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "As we can see from the bar chart, Sonder has around 10% of the 6140 listings, followed by Kara with 155, Bluebird with 154, and Mike with 142 listings. Those four hosts represent about 25% of the total listings. The other hosts' number of listings range from 1 to 89 listings.\n",
    "\n",
    "According to Inside Airbnb, it is vital to notice that \"A host may list separate rooms in the same apartment, or multiple apartments or homes available in their entirety. Hosts with multiple listings are more likely to be running a business, are unlikely to be living in the property, and in violation of most short term rental laws designed to protect residential housing.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q4. Which neighborhoods have the most number of listing in Boston?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dorchester                 536\n",
       "Jamaica Plain              513\n",
       "Back Bay                   492\n",
       "Downtown                   452\n",
       "South End                  439\n",
       "Fenway                     438\n",
       "Brighton                   358\n",
       "South Boston               337\n",
       "Allston                    329\n",
       "Roxbury                    319\n",
       "East Boston                311\n",
       "Beacon Hill                255\n",
       "North End                  244\n",
       "Mission Hill               214\n",
       "Charlestown                156\n",
       "West End                   154\n",
       "Chinatown                  139\n",
       "Roslindale                 117\n",
       "South Boston Waterfront     76\n",
       "Mattapan                    75\n",
       "West Roxbury                70\n",
       "Hyde Park                   59\n",
       "Bay Village                 39\n",
       "Longwood Medical Area       11\n",
       "Leather District             7\n",
       "Name: neighbourhood_cleansed, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_listings_prep1['neighbourhood_cleansed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/478.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_count = df_listings_prep1.groupby('neighbourhood_cleansed').count()['id']\n",
    "n_count = n_count.sort_values(ascending=False)\n",
    "x = n_count.index\n",
    "y = n_count\n",
    "y_cum = np.cumsum(y)\n",
    "y_perc = 100*y_cum/y.sum()\n",
    "\n",
    "trace1 = dict(type='bar',\n",
    "    x=x,\n",
    "    y=y,\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5)),\n",
    "    name='Count of Listings',\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "trace2 = dict(type='scatter',\n",
    "    x=x,\n",
    "    y=y_perc,\n",
    "    marker=dict(\n",
    "        color='#7F7F7F'\n",
    "    ),\n",
    "    line=dict(color= '#7F7F7F', width= 1.5),\n",
    "    name='Cumulative Listings Percent of Total (%)',\n",
    "    xaxis='x1', \n",
    "    yaxis='y2' \n",
    ")\n",
    "    \n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(\n",
    "    title='Number of Listings by neighbourhood',\n",
    "    legend= dict(x=-.1, y=1.2),\n",
    "    yaxis=dict(\n",
    "        title='Count of Listings'\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        title='Percent of Total Listings (%)',\n",
    "        range=[0,100],\n",
    "        overlaying='y',\n",
    "        anchor='x',\n",
    "        side='right'\n",
    "        )\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Percent of listings by neighbourhood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will investigate the price distribution to make sure outliers, hosts who list at above and beyond the reasonable prices per night."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/484.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_listings_prep1.price\n",
    "\n",
    "annotations={}\n",
    "\n",
    "trace1 = go.Histogram(\n",
    "    x=x,\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5)),\n",
    "    name='Price',\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title = \"Price Distributions\",\n",
    "    xaxis=dict(\n",
    "        title='Price'),\n",
    "    yaxis=dict(\n",
    "        title='Count'))\n",
    "\n",
    "layout.update(dict(annotations=[go.Annotation(text=\"Outliers all after\", x=1000, y=0)]))\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, annotations=annotations, filename='Price Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/500.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_listings_prep1.price[df_listings_prep1.price <=1000]\n",
    "\n",
    "trace1 = go.Histogram(\n",
    "    x=x,\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5)),\n",
    "    name='Price',\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title = \"Price Distributions <1000\",\n",
    "    xaxis=dict(\n",
    "        title='Price'),\n",
    "    yaxis=dict(\n",
    "        title='Count'))\n",
    "\n",
    "layout.update(dict(annotations=[go.Annotation(text=\"Outliers all after\", x=500, y=0)]))\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Price Distribution <1000')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/502.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_listings_prep1.price[df_listings_prep1.price <=500]\n",
    "\n",
    "trace1 = go.Histogram(\n",
    "    x=x,\n",
    "    marker=dict(\n",
    "        color='rgb(158,202,225)',\n",
    "        line=dict(\n",
    "            color='rgb(8,48,107)',\n",
    "            width=1.5)),\n",
    "    name='Price',\n",
    "    opacity=0.6\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title = \"Price Distributions <500\",\n",
    "    xaxis=dict(\n",
    "        title='Price'),\n",
    "    yaxis=dict(\n",
    "        title='Count'))\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Price Distribution <500')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the histogram that illustrates the price distribution among all listings, a maximum of 500 dollars per night is reasonable comparing with higher rates that have been dropped; it's high though, as the distribution explicates that most listings occur at the range between 50 up to 200 dollars per night. However, it will the cutpoint for the maximum rate per night in our further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500.0\n"
     ]
    }
   ],
   "source": [
    "#setting the final dataset to include only listings within the price up to $500\n",
    "df_listings_prep1 = df_listings_prep1[df_listings_prep1.price <=500]\n",
    "print(df_listings_prep1.price.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q5. Which neighborhoods are considered as the most expensive in Boston?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chinatown</th>\n",
       "      <td>130.0</td>\n",
       "      <td>226.430769</td>\n",
       "      <td>54.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Boston Waterfront</th>\n",
       "      <td>70.0</td>\n",
       "      <td>223.385714</td>\n",
       "      <td>49.0</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>136.0</td>\n",
       "      <td>218.867647</td>\n",
       "      <td>49.0</td>\n",
       "      <td>499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downtown</th>\n",
       "      <td>428.0</td>\n",
       "      <td>215.432243</td>\n",
       "      <td>49.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Back Bay</th>\n",
       "      <td>427.0</td>\n",
       "      <td>215.009368</td>\n",
       "      <td>40.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leather District</th>\n",
       "      <td>6.0</td>\n",
       "      <td>210.500000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fenway</th>\n",
       "      <td>429.0</td>\n",
       "      <td>200.037296</td>\n",
       "      <td>20.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlestown</th>\n",
       "      <td>147.0</td>\n",
       "      <td>192.360544</td>\n",
       "      <td>10.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Boston</th>\n",
       "      <td>322.0</td>\n",
       "      <td>191.214286</td>\n",
       "      <td>48.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South End</th>\n",
       "      <td>429.0</td>\n",
       "      <td>187.114219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mission Hill</th>\n",
       "      <td>204.0</td>\n",
       "      <td>181.039216</td>\n",
       "      <td>18.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bay Village</th>\n",
       "      <td>37.0</td>\n",
       "      <td>180.675676</td>\n",
       "      <td>95.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North End</th>\n",
       "      <td>238.0</td>\n",
       "      <td>176.369748</td>\n",
       "      <td>45.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beacon Hill</th>\n",
       "      <td>247.0</td>\n",
       "      <td>173.870445</td>\n",
       "      <td>50.0</td>\n",
       "      <td>499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica Plain</th>\n",
       "      <td>507.0</td>\n",
       "      <td>139.800789</td>\n",
       "      <td>21.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Boston</th>\n",
       "      <td>310.0</td>\n",
       "      <td>125.229032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allston</th>\n",
       "      <td>320.0</td>\n",
       "      <td>117.596875</td>\n",
       "      <td>10.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longwood Medical Area</th>\n",
       "      <td>11.0</td>\n",
       "      <td>115.727273</td>\n",
       "      <td>45.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roxbury</th>\n",
       "      <td>307.0</td>\n",
       "      <td>106.371336</td>\n",
       "      <td>17.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brighton</th>\n",
       "      <td>354.0</td>\n",
       "      <td>101.059322</td>\n",
       "      <td>22.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Roxbury</th>\n",
       "      <td>69.0</td>\n",
       "      <td>95.594203</td>\n",
       "      <td>33.0</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dorchester</th>\n",
       "      <td>528.0</td>\n",
       "      <td>92.871212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mattapan</th>\n",
       "      <td>75.0</td>\n",
       "      <td>84.520000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roslindale</th>\n",
       "      <td>117.0</td>\n",
       "      <td>84.094017</td>\n",
       "      <td>24.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyde Park</th>\n",
       "      <td>59.0</td>\n",
       "      <td>81.118644</td>\n",
       "      <td>25.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean   min    max\n",
       "neighbourhood_cleansed                                 \n",
       "Chinatown                130.0  226.430769  54.0  500.0\n",
       "South Boston Waterfront   70.0  223.385714  49.0  450.0\n",
       "West End                 136.0  218.867647  49.0  499.0\n",
       "Downtown                 428.0  215.432243  49.0  500.0\n",
       "Back Bay                 427.0  215.009368  40.0  500.0\n",
       "Leather District           6.0  210.500000  65.0  375.0\n",
       "Fenway                   429.0  200.037296  20.0  500.0\n",
       "Charlestown              147.0  192.360544  10.0  500.0\n",
       "South Boston             322.0  191.214286  48.0  500.0\n",
       "South End                429.0  187.114219   0.0  500.0\n",
       "Mission Hill             204.0  181.039216  18.0  500.0\n",
       "Bay Village               37.0  180.675676  95.0  500.0\n",
       "North End                238.0  176.369748  45.0  500.0\n",
       "Beacon Hill              247.0  173.870445  50.0  499.0\n",
       "Jamaica Plain            507.0  139.800789  21.0  500.0\n",
       "East Boston              310.0  125.229032   0.0  400.0\n",
       "Allston                  320.0  117.596875  10.0  500.0\n",
       "Longwood Medical Area     11.0  115.727273  45.0  196.0\n",
       "Roxbury                  307.0  106.371336  17.0  500.0\n",
       "Brighton                 354.0  101.059322  22.0  500.0\n",
       "West Roxbury              69.0   95.594203  33.0  350.0\n",
       "Dorchester               528.0   92.871212   0.0  500.0\n",
       "Mattapan                  75.0   84.520000  28.0  419.0\n",
       "Roslindale               117.0   84.094017  24.0  300.0\n",
       "Hyde Park                 59.0   81.118644  25.0  275.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_exp = df_listings_prep1.groupby('neighbourhood_cleansed')\n",
    "n_exp = n_exp['price'].describe().drop(['std', '25%', '50%', '75%'], axis=1).sort_values(by='mean', ascending=False)\n",
    "n_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#creating numpy arraies for the top six neighbourhoods' prices: 'Chinatown', 'South Boston Waterfront', 'West End', 'Downtown', 'Back Bay', and 'Fenway'\n",
    "#'Leather District' was skiped becuase it has only 6 listings, a very small number compared to other neighbourhoods' listings.\n",
    "\n",
    "chinatown_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'Chinatown']\n",
    "chinatown_p = np.array(chinatown_p.price)\n",
    "\n",
    "back_bay_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'Back Bay']\n",
    "back_bay_p = np.array(back_bay_p.price)\n",
    "\n",
    "west_end_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'West End']\n",
    "west_end_p = np.array(west_end_p.price)\n",
    "\n",
    "waterfront_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'South Boston Waterfront']\n",
    "waterfront_p = np.array(waterfront_p.price)\n",
    "\n",
    "downtown_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'Downtown']\n",
    "downtown_p = np.array(downtown_p.price)\n",
    "\n",
    "fenway_p = df_listings_prep1[df_listings_prep1.neighbourhood_cleansed == 'Fenway']\n",
    "fenway_p = np.array(fenway_p.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/482.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = ['Chinatown', 'West End', 'Downtown', 'South Boston Waterfront', 'Back Bay', 'Fenway']\n",
    "\n",
    "y0 = chinatown_p\n",
    "y1 = west_end_p\n",
    "y2 = downtown_p\n",
    "y3 = waterfront_p\n",
    "y4 = back_bay_p\n",
    "y5 = fenway_p\n",
    "\n",
    "y_data = [y0,y1,y2,y3,y4,y5]\n",
    "\n",
    "colors = ['rgba(93, 164, 214, 0.5)', 'rgba(255, 144, 14, 0.5)', 'rgba(44, 160, 101, 0.5)', 'rgba(255, 65, 54, 0.5)', 'rgba(207, 114, 255, 0.5)', 'rgba(127, 96, 0, 0.5)']\n",
    "\n",
    "traces = []\n",
    "\n",
    "for xd, yd, cls in zip(x_data, y_data, colors):\n",
    "        traces.append(go.Box(\n",
    "            y=yd,\n",
    "            name=xd,\n",
    "            boxmean=True,\n",
    "            boxpoints='all',\n",
    "            jitter=0.5,\n",
    "            whiskerwidth=0.2,\n",
    "            fillcolor=cls,\n",
    "            marker=dict(\n",
    "                size=2,\n",
    "            ),\n",
    "            line=dict(width=1),\n",
    "        ))\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Comparsion between the top most expensive neighborhoods',\n",
    "    yaxis=dict(\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=False,\n",
    "        dtick=40,\n",
    "        gridcolor='rgb(255, 255, 255)',\n",
    "        gridwidth=1,\n",
    "    ),\n",
    "    paper_bgcolor='rgb(243, 243, 243)',\n",
    "    plot_bgcolor='rgb(243, 243, 243)',\n",
    "    showlegend=False\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=traces, layout=layout)\n",
    "py.iplot(fig, filename='Most expensive neighbourhood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "While the bar plot shows that Dorchester and Jamaica Plain the top two neighborhoods with respect to the number of listings in the dataset, the box plots illustrate a complete comparison between the top most expensive neighborhoods that excludes the top two mentioned neighborhoods. The plots represent the table that describes the prices for each neighborhood.\n",
    "\n",
    "      1. Chinatown, which is part of the Downtown, has the highest average price per night around $226, while 'Fenway' has $200 per night among the six most expensive neighborhoods.\n",
    "      2. Most listings range between $289 per night and $131 per night, which indicates the reasonable price at one of these neighborhoods.\n",
    "      \n",
    "*The numbers will be elaborated even further with heatmap generated below as it shows the areas not only where the rates per night are high, but also where most of the listings are located.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb628d1613d9448fa244ec4bd0ec01be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get the locations from the data set\n",
    "locations = df_listings_prep1[['latitude', 'longitude']]\n",
    "#Get the price from the data\n",
    "prices = df_listings_prep1['price']\n",
    "#Set up your map\n",
    "fig = gmaps.figure()\n",
    "fig.add_layer(gmaps.heatmap_layer(locations, weights=prices))\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        \n",
    "<center> <img src=\"https://i.etsystatic.com/5206469/r/il/4273de/597430359/il_fullxfull.597430359_a122.jpg\" style=\"width:400px;\"/>\n",
    "\n",
    "Art by [Carrie Wagner](https://www.etsy.com/shop/SepiaLepus?ref=simple-shop-header-name&listing_id=208367563) on [Etsy](https://www.etsy.com/?ref=lgo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <font color=blue> Q6. Which are the popular neighbourhoods based on the average number of reviews?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North End</th>\n",
       "      <td>238.0</td>\n",
       "      <td>68.184874</td>\n",
       "      <td>491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Boston</th>\n",
       "      <td>310.0</td>\n",
       "      <td>56.748387</td>\n",
       "      <td>444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dorchester</th>\n",
       "      <td>528.0</td>\n",
       "      <td>42.458333</td>\n",
       "      <td>513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beacon Hill</th>\n",
       "      <td>247.0</td>\n",
       "      <td>41.170040</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roxbury</th>\n",
       "      <td>307.0</td>\n",
       "      <td>38.068404</td>\n",
       "      <td>371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South End</th>\n",
       "      <td>429.0</td>\n",
       "      <td>37.079254</td>\n",
       "      <td>358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica Plain</th>\n",
       "      <td>507.0</td>\n",
       "      <td>33.781065</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Boston</th>\n",
       "      <td>322.0</td>\n",
       "      <td>32.739130</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlestown</th>\n",
       "      <td>147.0</td>\n",
       "      <td>31.346939</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Back Bay</th>\n",
       "      <td>427.0</td>\n",
       "      <td>29.292740</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downtown</th>\n",
       "      <td>428.0</td>\n",
       "      <td>26.116822</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brighton</th>\n",
       "      <td>354.0</td>\n",
       "      <td>25.864407</td>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allston</th>\n",
       "      <td>320.0</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinatown</th>\n",
       "      <td>130.0</td>\n",
       "      <td>19.684615</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fenway</th>\n",
       "      <td>429.0</td>\n",
       "      <td>16.587413</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>136.0</td>\n",
       "      <td>14.580882</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mission Hill</th>\n",
       "      <td>204.0</td>\n",
       "      <td>13.740196</td>\n",
       "      <td>626.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count       mean    max\n",
       "neighbourhood_cleansed                         \n",
       "North End               238.0  68.184874  491.0\n",
       "East Boston             310.0  56.748387  444.0\n",
       "Dorchester              528.0  42.458333  513.0\n",
       "Beacon Hill             247.0  41.170040  290.0\n",
       "Roxbury                 307.0  38.068404  371.0\n",
       "South End               429.0  37.079254  358.0\n",
       "Jamaica Plain           507.0  33.781065  424.0\n",
       "South Boston            322.0  32.739130  413.0\n",
       "Charlestown             147.0  31.346939  321.0\n",
       "Back Bay                427.0  29.292740  507.0\n",
       "Downtown                428.0  26.116822  299.0\n",
       "Brighton                354.0  25.864407  299.0\n",
       "Allston                 320.0  22.500000  257.0\n",
       "Chinatown               130.0  19.684615  167.0\n",
       "Fenway                  429.0  16.587413  232.0\n",
       "West End                136.0  14.580882  205.0\n",
       "Mission Hill            204.0  13.740196  626.0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity = df_listings_prep1.groupby('neighbourhood_cleansed')\n",
    "popularity = popularity['number_of_reviews'].describe().drop(['std', '25%', '50%', 'min', '75%'], axis=1).sort_values(by='mean', ascending=False)\n",
    "popularity = popularity[popularity['count'] > 120]\n",
    "popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The results for the popular neighborhoods based on the average number of reviews is embellished with the heatmap generated below as it confirms that North Ends has the highest reviews although it doesn't have the highest number of listings.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90f339c80bfc4275b5bb9654917694e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get the locations from the data set\n",
    "locations = df_listings_prep1[['latitude', 'longitude']]\n",
    "#Get the price from the data\n",
    "prices = df_listings_prep1['number_of_reviews']\n",
    "#Set up your map\n",
    "fig = gmaps.figure()\n",
    "fig.add_layer(gmaps.heatmap_layer(locations, weights=prices))\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "### <font color=blue> Q7. Which type of room has the majority of listings in Boston airbnb?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    3775\n",
       "Private room       2061\n",
       "Shared room          71\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms_df = df_listings_prep1['room_type'].value_counts()\n",
    "rooms_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/plotly/graph_objs/_deprecations.py:39: DeprecationWarning:\n",
      "\n",
      "plotly.graph_objs.Data is deprecated.\n",
      "Please replace it with a list or tuple of instances of the following types\n",
      "  - plotly.graph_objs.Scatter\n",
      "  - plotly.graph_objs.Bar\n",
      "  - plotly.graph_objs.Area\n",
      "  - plotly.graph_objs.Histogram\n",
      "  - etc.\n",
      "\n",
      "\n",
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/480.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms = df_listings_prep1['room_type'].value_counts()\n",
    "labels = rooms_df.index\n",
    "values = rooms\n",
    "colors = ['slateblue', 'aquamarine', 'mediumblue']\n",
    "\n",
    "\n",
    "trace1 = go.Pie(labels=labels, values=round(100*values/values.sum(), 2),\n",
    "               hoverinfo='label+percent', textinfo='value', \n",
    "               textfont=dict(size=20),\n",
    "               hole=0.9,\n",
    "               showlegend=False,\n",
    "               opacity=0.3,\n",
    "               marker=dict(colors=colors, \n",
    "                           line=dict(color='#000000', width=2)))\n",
    "\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=[rooms_df.index[0]],\n",
    "    y=[4002],\n",
    "    name=rooms_df.index[0],\n",
    "    marker=dict(\n",
    "        color='slateblue',\n",
    "        line=dict(\n",
    "            color='slateblue',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.4\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=[rooms_df.index[1]],\n",
    "    y=[2066],\n",
    "    name=rooms_df.index[1],\n",
    "    marker=dict(\n",
    "        color='aquamarine',\n",
    "        line=dict(\n",
    "            color='aquamarine',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.4\n",
    ")\n",
    "\n",
    "trace4 = go.Bar(\n",
    "    x=[rooms_df.index[2]],\n",
    "    y=[72],\n",
    "    name=rooms_df.index[2],\n",
    "    marker=dict(\n",
    "        color='mediumblue',\n",
    "        line=dict(\n",
    "            color='mediumblue',\n",
    "            width=1.5,\n",
    "        )\n",
    "    ),\n",
    "    opacity=0.3\n",
    ")\n",
    "\n",
    "data = go.Data([trace1, trace2, trace3, trace4])\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Count of room type',\n",
    "    xaxis=dict(\n",
    "        title='Room Type',\n",
    "        domain=[0.4, 0.6]\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='Count',\n",
    "        domain=[0.4, 0.7]\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='percent of room types')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Airbnb hosts can list one or all of these room types: entire homes/apartments, private or shared rooms. The donut plot shows that entire homes/apartments accounts for 63.9% of the listings with 3775 listings around Boston. Following by private room with 2061 listings and shared room with only 71 listings account for only 1.2%\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#copying the latest dataset\n",
    "df_listings_prep2 = df_listings_prep1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'host_name', 'host_is_superhost', 'neighbourhood_cleansed',\n",
       "       'zipcode', 'property_type', 'room_type', 'accommodates', 'bathrooms',\n",
       "       'bedrooms', 'beds', 'amenities', 'price', 'cleaning_fee',\n",
       "       'number_of_reviews', 'instant_bookable', 'cancellation_policy',\n",
       "       'latitude', 'longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_listings_prep2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>amenities</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5066</th>\n",
       "      <td>28218323</td>\n",
       "      <td>0</td>\n",
       "      <td>Chinatown</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Elevator,H...</td>\n",
       "      <td>249.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>4568116</td>\n",
       "      <td>1</td>\n",
       "      <td>Roxbury</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>130.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>0</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1545185</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Guest suite</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075</th>\n",
       "      <td>15451419</td>\n",
       "      <td>0</td>\n",
       "      <td>South End</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,Elevator,H...</td>\n",
       "      <td>320.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3441</th>\n",
       "      <td>21648941</td>\n",
       "      <td>0</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  host_is_superhost neighbourhood_cleansed property_type  \\\n",
       "5066  28218323                  0              Chinatown     Apartment   \n",
       "536    4568116                  1                Roxbury     Townhouse   \n",
       "235    1545185                  0               Brighton   Guest suite   \n",
       "2075  15451419                  0              South End     Apartment   \n",
       "3441  21648941                  0               Brighton         House   \n",
       "\n",
       "            room_type  accommodates  bathrooms  bedrooms  beds  \\\n",
       "5066  Entire home/apt           4.0        1.0       1.0   2.0   \n",
       "536   Entire home/apt           2.0        1.0       0.0   1.0   \n",
       "235   Entire home/apt           4.0        1.0       1.0   2.0   \n",
       "2075  Entire home/apt           3.0        2.0       2.0   2.0   \n",
       "3441     Private room           2.0        1.5       1.0   1.0   \n",
       "\n",
       "                                              amenities  price  cleaning_fee  \\\n",
       "5066  {TV,Wifi,\"Air conditioning\",Kitchen,Elevator,H...  249.0          59.0   \n",
       "536   {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  130.0          30.0   \n",
       "235   {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...  140.0         100.0   \n",
       "2075  {TV,Wifi,\"Air conditioning\",Kitchen,Elevator,H...  320.0          20.0   \n",
       "3441  {TV,Wifi,\"Air conditioning\",Kitchen,\"Free stre...   48.0          30.0   \n",
       "\n",
       "      number_of_reviews  instant_bookable cancellation_policy  \n",
       "5066                6.0                 1            moderate  \n",
       "536               371.0                 0            moderate  \n",
       "235                92.0                 1            moderate  \n",
       "2075                9.0                 1            moderate  \n",
       "3441                1.0                 1            moderate  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping two features not needed for further analysis\n",
    "df_listings_prep2 = df_listings_prep2.drop(['host_name', 'zipcode', 'latitude', 'longitude'], axis=1)\n",
    "df_listings_prep2.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5576                                                   {}\n",
       "1596    {TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Paid p...\n",
       "1117    {TV,Internet,Wifi,\"Air conditioning\",Kitchen,\"...\n",
       "3570    {TV,Wifi,\"Air conditioning\",Kitchen,Elevator,H...\n",
       "680     {TV,\"Cable TV\",Wifi,\"Air conditioning\",\"Free s...\n",
       "315     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...\n",
       "4685    {TV,\"Cable TV\",Wifi,\"Air conditioning\",Kitchen...\n",
       "2944    {TV,Internet,Wifi,\"Air conditioning\",Kitchen,H...\n",
       "3161    {TV,Wifi,Kitchen,\"Free street parking\",Heating...\n",
       "120     {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...\n",
       "Name: amenities, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assessing the amenities feature\n",
    "df_listings_prep2.amenities.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 120)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>24-hourcheck-in</th>\n",
       "      <th>Accessible-heightbed</th>\n",
       "      <th>Accessible-heighttoilet</th>\n",
       "      <th>Airconditioning</th>\n",
       "      <th>BBQgrill</th>\n",
       "      <th>Babybath</th>\n",
       "      <th>Babymonitor</th>\n",
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       "      <th>Bathtub</th>\n",
       "      <th>Bathtubwithbathchair</th>\n",
       "      <th>...</th>\n",
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       "      <th>Windowguards</th>\n",
       "      <th>toilet</th>\n",
       "      <th>translationmissing:en.hosting_amenity_49</th>\n",
       "      <th>translationmissing:en.hosting_amenity_50</th>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>2 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      24-hourcheck-in  Accessible-heightbed  Accessible-heighttoilet  \\\n",
       "3623                0                     0                        0   \n",
       "209                 1                     0                        0   \n",
       "\n",
       "      Airconditioning  BBQgrill  Babybath  Babymonitor  \\\n",
       "3623                0         0         0            0   \n",
       "209                 1         0         0            0   \n",
       "\n",
       "      Babysitterrecommendations  Bathtub  Bathtubwithbathchair  ...  \\\n",
       "3623                          0        0                     0  ...   \n",
       "209                           0        1                     0  ...   \n",
       "\n",
       "      Wideclearancetobed  Wideclearancetoshower  Widedoorway  Wideentryway  \\\n",
       "3623                   0                      0            0             0   \n",
       "209                    0                      0            0             0   \n",
       "\n",
       "      Widehallwayclearance  Wifi  Windowguards  toilet  \\\n",
       "3623                     0     1             0       0   \n",
       "209                      0     1             0       0   \n",
       "\n",
       "      translationmissing:en.hosting_amenity_49  \\\n",
       "3623                                         0   \n",
       "209                                          0   \n",
       "\n",
       "      translationmissing:en.hosting_amenity_50  \n",
       "3623                                         0  \n",
       "209                                          0  \n",
       "\n",
       "[2 rows x 120 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#splitting the amenities and then creating dummies\n",
    "df_listings_prep2['amenities'] = df_listings_prep2['amenities'].str.replace('[{}\" ]', '')\n",
    "df_amenities = df_listings_prep2.amenities.str.get_dummies(sep = \",\")\n",
    "print(df_amenities.shape)\n",
    "df_amenities.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 55)\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>neighbourhood_cleansed_Allston</th>\n",
       "      <th>neighbourhood_cleansed_Back Bay</th>\n",
       "      <th>neighbourhood_cleansed_Bay Village</th>\n",
       "      <th>neighbourhood_cleansed_Beacon Hill</th>\n",
       "      <th>neighbourhood_cleansed_Brighton</th>\n",
       "      <th>neighbourhood_cleansed_Charlestown</th>\n",
       "      <th>neighbourhood_cleansed_Chinatown</th>\n",
       "      <th>neighbourhood_cleansed_Dorchester</th>\n",
       "      <th>neighbourhood_cleansed_Downtown</th>\n",
       "      <th>neighbourhood_cleansed_East Boston</th>\n",
       "      <th>...</th>\n",
       "      <th>property_type_Villa</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
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       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
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      ],
      "text/plain": [
       "      neighbourhood_cleansed_Allston  neighbourhood_cleansed_Back Bay  \\\n",
       "4958                               0                                1   \n",
       "692                                0                                1   \n",
       "\n",
       "      neighbourhood_cleansed_Bay Village  neighbourhood_cleansed_Beacon Hill  \\\n",
       "4958                                   0                                   0   \n",
       "692                                    0                                   0   \n",
       "\n",
       "      neighbourhood_cleansed_Brighton  neighbourhood_cleansed_Charlestown  \\\n",
       "4958                                0                                   0   \n",
       "692                                 0                                   0   \n",
       "\n",
       "      neighbourhood_cleansed_Chinatown  neighbourhood_cleansed_Dorchester  \\\n",
       "4958                                 0                                  0   \n",
       "692                                  0                                  0   \n",
       "\n",
       "      neighbourhood_cleansed_Downtown  neighbourhood_cleansed_East Boston  \\\n",
       "4958                                0                                   0   \n",
       "692                                 0                                   0   \n",
       "\n",
       "      ...  property_type_Villa  room_type_Entire home/apt  \\\n",
       "4958  ...                    0                          1   \n",
       "692   ...                    0                          1   \n",
       "\n",
       "      room_type_Private room  room_type_Shared room  \\\n",
       "4958                       0                      0   \n",
       "692                        0                      0   \n",
       "\n",
       "      cancellation_policy_flexible  cancellation_policy_moderate  \\\n",
       "4958                             0                             0   \n",
       "692                              0                             0   \n",
       "\n",
       "      cancellation_policy_strict  \\\n",
       "4958                           0   \n",
       "692                            0   \n",
       "\n",
       "      cancellation_policy_strict_14_with_grace_period  \\\n",
       "4958                                                1   \n",
       "692                                                 1   \n",
       "\n",
       "      cancellation_policy_super_strict_30  cancellation_policy_super_strict_60  \n",
       "4958                                    0                                    0  \n",
       "692                                     0                                    0  \n",
       "\n",
       "[2 rows x 55 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preparing the categorical features that will be used for modeling\n",
    "features_cat = df_listings_prep2.select_dtypes(include=['object'])\n",
    "features_cat = features_cat.drop(['amenities'], axis=1) #dropping the amenities feature as it has already split into 121 dummies\n",
    "\n",
    "#getting dummies for each categorical feature except amenities\n",
    "features_cat = pd.get_dummies(features_cat)\n",
    "\n",
    "#viewing the dataset after creatign the dummies\n",
    "print(features_cat.shape)\n",
    "features_cat.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
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       "      <td>125.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            id  host_is_superhost  accommodates  bathrooms  bedrooms  beds  \\\n",
       "5615  29901892                  1           2.0        1.0       1.0   1.0   \n",
       "1600  13140867                  0           2.0        1.0       1.0   1.0   \n",
       "\n",
       "      price  cleaning_fee  number_of_reviews  instant_bookable  \n",
       "5615   52.0          14.0               10.0                 1  \n",
       "1600  250.0         125.0              133.0                 1  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preparing the numerical features that will be used for modeling\n",
    "features_num = df_listings_prep2.select_dtypes(include=['float', 'int'])\n",
    "features_num_lst = features_num.columns\n",
    "print(features_num.shape)\n",
    "list(features_num_lst)\n",
    "features_num.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>...</th>\n",
       "      <th>Wideclearancetobed</th>\n",
       "      <th>Wideclearancetoshower</th>\n",
       "      <th>Widedoorway</th>\n",
       "      <th>Wideentryway</th>\n",
       "      <th>Widehallwayclearance</th>\n",
       "      <th>Wifi</th>\n",
       "      <th>Windowguards</th>\n",
       "      <th>toilet</th>\n",
       "      <th>translationmissing:en.hosting_amenity_49</th>\n",
       "      <th>translationmissing:en.hosting_amenity_50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3032</th>\n",
       "      <td>20228320</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>13685207</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2848</th>\n",
       "      <td>19455818</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>5259996</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>5.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6020</th>\n",
       "      <td>31571124</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5469</th>\n",
       "      <td>29539009</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4075</th>\n",
       "      <td>23467213</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5922</th>\n",
       "      <td>31073977</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5288</th>\n",
       "      <td>29014832</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>12581300</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 185 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  host_is_superhost  accommodates  bathrooms  bedrooms  beds  \\\n",
       "3032  20228320                  1           3.0        1.0       1.0   1.0   \n",
       "1717  13685207                  0           6.0        2.0       3.0   3.0   \n",
       "2848  19455818                  0           5.0        2.0       2.0   1.0   \n",
       "618    5259996                  0           3.0        1.5       2.0   2.0   \n",
       "6020  31571124                  0           5.0        1.0       2.0   3.0   \n",
       "5469  29539009                  0           4.0        1.0       0.0   1.0   \n",
       "4075  23467213                  0           6.0        3.0       3.0   3.0   \n",
       "5922  31073977                  1           2.0        2.0       1.0   1.0   \n",
       "5288  29014832                  0           2.0        1.0       1.0   1.0   \n",
       "1520  12581300                  0           2.0        1.0       1.0   1.0   \n",
       "\n",
       "      price  cleaning_fee  number_of_reviews  instant_bookable  ...  \\\n",
       "3032  195.0          80.0               86.0                 1  ...   \n",
       "1717  184.0         154.0               27.0                 1  ...   \n",
       "2848  469.0         100.0                0.0                 0  ...   \n",
       "618   245.0         175.0                5.0                 0  ...   \n",
       "6020  125.0          69.0                0.0                 1  ...   \n",
       "5469  133.0         100.0                0.0                 0  ...   \n",
       "4075  405.0         147.0               17.0                 1  ...   \n",
       "5922   44.0          26.0                2.0                 0  ...   \n",
       "5288   70.0          15.0                0.0                 1  ...   \n",
       "1520   65.0           5.0               11.0                 0  ...   \n",
       "\n",
       "      Wideclearancetobed  Wideclearancetoshower  Widedoorway  Wideentryway  \\\n",
       "3032                   0                      0            0             0   \n",
       "1717                   0                      0            0             0   \n",
       "2848                   0                      0            0             0   \n",
       "618                    0                      0            0             0   \n",
       "6020                   0                      0            0             0   \n",
       "5469                   0                      0            0             0   \n",
       "4075                   0                      0            0             0   \n",
       "5922                   0                      0            0             0   \n",
       "5288                   0                      0            0             0   \n",
       "1520                   0                      0            0             0   \n",
       "\n",
       "      Widehallwayclearance  Wifi  Windowguards  toilet  \\\n",
       "3032                     0     1             0       0   \n",
       "1717                     0     1             0       0   \n",
       "2848                     0     1             0       0   \n",
       "618                      0     1             0       0   \n",
       "6020                     0     1             0       0   \n",
       "5469                     0     1             0       0   \n",
       "4075                     0     1             0       0   \n",
       "5922                     0     1             0       0   \n",
       "5288                     0     1             0       0   \n",
       "1520                     0     1             0       0   \n",
       "\n",
       "      translationmissing:en.hosting_amenity_49  \\\n",
       "3032                                         0   \n",
       "1717                                         0   \n",
       "2848                                         0   \n",
       "618                                          0   \n",
       "6020                                         0   \n",
       "5469                                         0   \n",
       "4075                                         0   \n",
       "5922                                         0   \n",
       "5288                                         0   \n",
       "1520                                         0   \n",
       "\n",
       "      translationmissing:en.hosting_amenity_50  \n",
       "3032                                         0  \n",
       "1717                                         0  \n",
       "2848                                         0  \n",
       "618                                          0  \n",
       "6020                                         0  \n",
       "5469                                         0  \n",
       "4075                                         0  \n",
       "5922                                         0  \n",
       "5288                                         0  \n",
       "1520                                         1  \n",
       "\n",
       "[10 rows x 185 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging all the prepared dataframes created: df_amenities, features_cat, and features_num\n",
    "df = pd.concat([features_num, features_cat, df_amenities], axis=1, join='inner')\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 185)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'host_is_superhost',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'price',\n",
       " 'cleaning_fee',\n",
       " 'number_of_reviews',\n",
       " 'instant_bookable',\n",
       " 'neighbourhood_cleansed_Allston',\n",
       " 'neighbourhood_cleansed_Back Bay',\n",
       " 'neighbourhood_cleansed_Bay Village',\n",
       " 'neighbourhood_cleansed_Beacon Hill',\n",
       " 'neighbourhood_cleansed_Brighton',\n",
       " 'neighbourhood_cleansed_Charlestown',\n",
       " 'neighbourhood_cleansed_Chinatown',\n",
       " 'neighbourhood_cleansed_Dorchester',\n",
       " 'neighbourhood_cleansed_Downtown',\n",
       " 'neighbourhood_cleansed_East Boston',\n",
       " 'neighbourhood_cleansed_Fenway',\n",
       " 'neighbourhood_cleansed_Hyde Park',\n",
       " 'neighbourhood_cleansed_Jamaica Plain',\n",
       " 'neighbourhood_cleansed_Leather District',\n",
       " 'neighbourhood_cleansed_Longwood Medical Area',\n",
       " 'neighbourhood_cleansed_Mattapan',\n",
       " 'neighbourhood_cleansed_Mission Hill',\n",
       " 'neighbourhood_cleansed_North End',\n",
       " 'neighbourhood_cleansed_Roslindale',\n",
       " 'neighbourhood_cleansed_Roxbury',\n",
       " 'neighbourhood_cleansed_South Boston',\n",
       " 'neighbourhood_cleansed_South Boston Waterfront',\n",
       " 'neighbourhood_cleansed_South End',\n",
       " 'neighbourhood_cleansed_West End',\n",
       " 'neighbourhood_cleansed_West Roxbury',\n",
       " 'property_type_Aparthotel',\n",
       " 'property_type_Apartment',\n",
       " 'property_type_Barn',\n",
       " 'property_type_Bed and breakfast',\n",
       " 'property_type_Boat',\n",
       " 'property_type_Boutique hotel',\n",
       " 'property_type_Bungalow',\n",
       " 'property_type_Camper/RV',\n",
       " 'property_type_Chalet',\n",
       " 'property_type_Condominium',\n",
       " 'property_type_Guest suite',\n",
       " 'property_type_Guesthouse',\n",
       " 'property_type_Hotel',\n",
       " 'property_type_House',\n",
       " 'property_type_Loft',\n",
       " 'property_type_Other',\n",
       " 'property_type_Resort',\n",
       " 'property_type_Serviced apartment',\n",
       " 'property_type_Tiny house',\n",
       " 'property_type_Townhouse',\n",
       " 'property_type_Villa',\n",
       " 'room_type_Entire home/apt',\n",
       " 'room_type_Private room',\n",
       " 'room_type_Shared room',\n",
       " 'cancellation_policy_flexible',\n",
       " 'cancellation_policy_moderate',\n",
       " 'cancellation_policy_strict',\n",
       " 'cancellation_policy_strict_14_with_grace_period',\n",
       " 'cancellation_policy_super_strict_30',\n",
       " 'cancellation_policy_super_strict_60',\n",
       " '24-hourcheck-in',\n",
       " 'Accessible-heightbed',\n",
       " 'Accessible-heighttoilet',\n",
       " 'Airconditioning',\n",
       " 'BBQgrill',\n",
       " 'Babybath',\n",
       " 'Babymonitor',\n",
       " 'Babysitterrecommendations',\n",
       " 'Bathtub',\n",
       " 'Bathtubwithbathchair',\n",
       " 'Beachessentials',\n",
       " 'Beachfront',\n",
       " 'Bedlinens',\n",
       " 'Breakfast',\n",
       " 'Buildingstaff',\n",
       " 'Buzzer/wirelessintercom',\n",
       " 'CableTV',\n",
       " 'Carbonmonoxidedetector',\n",
       " 'Cat(s)',\n",
       " 'Changingtable',\n",
       " 'Children’sbooksandtoys',\n",
       " 'Children’sdinnerware',\n",
       " 'Cleaningbeforecheckout',\n",
       " 'Coffeemaker',\n",
       " 'Cookingbasics',\n",
       " 'Crib',\n",
       " 'Disabledparkingspot',\n",
       " 'Dishesandsilverware',\n",
       " 'Dishwasher',\n",
       " 'Dog(s)',\n",
       " 'Doorman',\n",
       " 'Dryer',\n",
       " 'EVcharger',\n",
       " 'Electricprofilingbed',\n",
       " 'Elevator',\n",
       " 'Essentials',\n",
       " 'Ethernetconnection',\n",
       " 'Extrapillowsandblankets',\n",
       " 'Family/kidfriendly',\n",
       " 'Fireextinguisher',\n",
       " 'Fireplaceguards',\n",
       " 'Firmmattress',\n",
       " 'Firstaidkit',\n",
       " 'Fixedgrabbarsforshower',\n",
       " 'Fixedgrabbarsfortoilet',\n",
       " 'Flatpathtofrontdoor',\n",
       " 'Freeparkingonpremises',\n",
       " 'Freestreetparking',\n",
       " 'Gameconsole',\n",
       " 'Gardenorbackyard',\n",
       " 'Gym',\n",
       " 'Hairdryer',\n",
       " 'Handheldshowerhead',\n",
       " 'Hangers',\n",
       " 'Heating',\n",
       " 'Highchair',\n",
       " 'Hostgreetsyou',\n",
       " 'Hottub',\n",
       " 'Hotwater',\n",
       " 'Hotwaterkettle',\n",
       " 'Indoorfireplace',\n",
       " 'Internet',\n",
       " 'Iron',\n",
       " 'Keypad',\n",
       " 'Kitchen',\n",
       " 'Lakeaccess',\n",
       " 'Laptopfriendlyworkspace',\n",
       " 'Lockbox',\n",
       " 'Lockonbedroomdoor',\n",
       " 'Longtermstaysallowed',\n",
       " 'Luggagedropoffallowed',\n",
       " 'Microwave',\n",
       " 'Other',\n",
       " 'Otherpet(s)',\n",
       " 'Outletcovers',\n",
       " 'Oven',\n",
       " 'Pack’nPlay/travelcrib',\n",
       " 'Paidparkingoffpremises',\n",
       " 'Paidparkingonpremises',\n",
       " 'Patioorbalcony',\n",
       " 'Petsallowed',\n",
       " 'Petsliveonthisproperty',\n",
       " 'Pocketwifi',\n",
       " 'Pool',\n",
       " 'Privatebathroom',\n",
       " 'Privateentrance',\n",
       " 'Privatelivingroom',\n",
       " 'Refrigerator',\n",
       " 'Roll-inshower',\n",
       " 'Room-darkeningshades',\n",
       " 'Safetycard',\n",
       " 'Selfcheck-in',\n",
       " 'Shampoo',\n",
       " 'Showerchair',\n",
       " 'Singlelevelhome',\n",
       " 'Ski-in/Ski-out',\n",
       " 'Smartlock',\n",
       " 'Smokedetector',\n",
       " 'Smokingallowed',\n",
       " 'Stairgates',\n",
       " 'Step-freeaccess',\n",
       " 'Stove',\n",
       " 'Suitableforevents',\n",
       " 'TV',\n",
       " 'Tablecornerguards',\n",
       " 'Washer',\n",
       " 'Washer/Dryer',\n",
       " 'Waterfront',\n",
       " 'Well-litpathtoentrance',\n",
       " 'Wheelchairaccessible',\n",
       " 'Wideclearancetobed',\n",
       " 'Wideclearancetoshower',\n",
       " 'Widedoorway',\n",
       " 'Wideentryway',\n",
       " 'Widehallwayclearance',\n",
       " 'Wifi',\n",
       " 'Windowguards',\n",
       " 'toilet',\n",
       " 'translationmissing:en.hosting_amenity_49',\n",
       " 'translationmissing:en.hosting_amenity_50']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirming the entire dataframes have been merged correctly with the right list of numerical features, amenities, and dummies for categorical features\n",
    "print(df.shape)\n",
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if we have any missing value for any feature\n",
    "df.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "# <center>STAGE THREE</center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "## 4. Data Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "#copying the latest dataset\n",
    "df_model = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 185)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'host_is_superhost',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'price',\n",
       " 'cleaning_fee',\n",
       " 'number_of_reviews',\n",
       " 'instant_bookable',\n",
       " 'neighbourhood_cleansed_Allston',\n",
       " 'neighbourhood_cleansed_Back Bay',\n",
       " 'neighbourhood_cleansed_Bay Village',\n",
       " 'neighbourhood_cleansed_Beacon Hill',\n",
       " 'neighbourhood_cleansed_Brighton',\n",
       " 'neighbourhood_cleansed_Charlestown',\n",
       " 'neighbourhood_cleansed_Chinatown',\n",
       " 'neighbourhood_cleansed_Dorchester',\n",
       " 'neighbourhood_cleansed_Downtown',\n",
       " 'neighbourhood_cleansed_East Boston',\n",
       " 'neighbourhood_cleansed_Fenway',\n",
       " 'neighbourhood_cleansed_Hyde Park',\n",
       " 'neighbourhood_cleansed_Jamaica Plain',\n",
       " 'neighbourhood_cleansed_Leather District',\n",
       " 'neighbourhood_cleansed_Longwood Medical Area',\n",
       " 'neighbourhood_cleansed_Mattapan',\n",
       " 'neighbourhood_cleansed_Mission Hill',\n",
       " 'neighbourhood_cleansed_North End',\n",
       " 'neighbourhood_cleansed_Roslindale',\n",
       " 'neighbourhood_cleansed_Roxbury',\n",
       " 'neighbourhood_cleansed_South Boston',\n",
       " 'neighbourhood_cleansed_South Boston Waterfront',\n",
       " 'neighbourhood_cleansed_South End',\n",
       " 'neighbourhood_cleansed_West End',\n",
       " 'neighbourhood_cleansed_West Roxbury',\n",
       " 'property_type_Aparthotel',\n",
       " 'property_type_Apartment',\n",
       " 'property_type_Barn',\n",
       " 'property_type_Bed and breakfast',\n",
       " 'property_type_Boat',\n",
       " 'property_type_Boutique hotel',\n",
       " 'property_type_Bungalow',\n",
       " 'property_type_Camper/RV',\n",
       " 'property_type_Chalet',\n",
       " 'property_type_Condominium',\n",
       " 'property_type_Guest suite',\n",
       " 'property_type_Guesthouse',\n",
       " 'property_type_Hotel',\n",
       " 'property_type_House',\n",
       " 'property_type_Loft',\n",
       " 'property_type_Other',\n",
       " 'property_type_Resort',\n",
       " 'property_type_Serviced apartment',\n",
       " 'property_type_Tiny house',\n",
       " 'property_type_Townhouse',\n",
       " 'property_type_Villa',\n",
       " 'room_type_Entire home/apt',\n",
       " 'room_type_Private room',\n",
       " 'room_type_Shared room',\n",
       " 'cancellation_policy_flexible',\n",
       " 'cancellation_policy_moderate',\n",
       " 'cancellation_policy_strict',\n",
       " 'cancellation_policy_strict_14_with_grace_period',\n",
       " 'cancellation_policy_super_strict_30',\n",
       " 'cancellation_policy_super_strict_60',\n",
       " '24-hourcheck-in',\n",
       " 'Accessible-heightbed',\n",
       " 'Accessible-heighttoilet',\n",
       " 'Airconditioning',\n",
       " 'BBQgrill',\n",
       " 'Babybath',\n",
       " 'Babymonitor',\n",
       " 'Babysitterrecommendations',\n",
       " 'Bathtub',\n",
       " 'Bathtubwithbathchair',\n",
       " 'Beachessentials',\n",
       " 'Beachfront',\n",
       " 'Bedlinens',\n",
       " 'Breakfast',\n",
       " 'Buildingstaff',\n",
       " 'Buzzer/wirelessintercom',\n",
       " 'CableTV',\n",
       " 'Carbonmonoxidedetector',\n",
       " 'Cat(s)',\n",
       " 'Changingtable',\n",
       " 'Children’sbooksandtoys',\n",
       " 'Children’sdinnerware',\n",
       " 'Cleaningbeforecheckout',\n",
       " 'Coffeemaker',\n",
       " 'Cookingbasics',\n",
       " 'Crib',\n",
       " 'Disabledparkingspot',\n",
       " 'Dishesandsilverware',\n",
       " 'Dishwasher',\n",
       " 'Dog(s)',\n",
       " 'Doorman',\n",
       " 'Dryer',\n",
       " 'EVcharger',\n",
       " 'Electricprofilingbed',\n",
       " 'Elevator',\n",
       " 'Essentials',\n",
       " 'Ethernetconnection',\n",
       " 'Extrapillowsandblankets',\n",
       " 'Family/kidfriendly',\n",
       " 'Fireextinguisher',\n",
       " 'Fireplaceguards',\n",
       " 'Firmmattress',\n",
       " 'Firstaidkit',\n",
       " 'Fixedgrabbarsforshower',\n",
       " 'Fixedgrabbarsfortoilet',\n",
       " 'Flatpathtofrontdoor',\n",
       " 'Freeparkingonpremises',\n",
       " 'Freestreetparking',\n",
       " 'Gameconsole',\n",
       " 'Gardenorbackyard',\n",
       " 'Gym',\n",
       " 'Hairdryer',\n",
       " 'Handheldshowerhead',\n",
       " 'Hangers',\n",
       " 'Heating',\n",
       " 'Highchair',\n",
       " 'Hostgreetsyou',\n",
       " 'Hottub',\n",
       " 'Hotwater',\n",
       " 'Hotwaterkettle',\n",
       " 'Indoorfireplace',\n",
       " 'Internet',\n",
       " 'Iron',\n",
       " 'Keypad',\n",
       " 'Kitchen',\n",
       " 'Lakeaccess',\n",
       " 'Laptopfriendlyworkspace',\n",
       " 'Lockbox',\n",
       " 'Lockonbedroomdoor',\n",
       " 'Longtermstaysallowed',\n",
       " 'Luggagedropoffallowed',\n",
       " 'Microwave',\n",
       " 'Other',\n",
       " 'Otherpet(s)',\n",
       " 'Outletcovers',\n",
       " 'Oven',\n",
       " 'Pack’nPlay/travelcrib',\n",
       " 'Paidparkingoffpremises',\n",
       " 'Paidparkingonpremises',\n",
       " 'Patioorbalcony',\n",
       " 'Petsallowed',\n",
       " 'Petsliveonthisproperty',\n",
       " 'Pocketwifi',\n",
       " 'Pool',\n",
       " 'Privatebathroom',\n",
       " 'Privateentrance',\n",
       " 'Privatelivingroom',\n",
       " 'Refrigerator',\n",
       " 'Roll-inshower',\n",
       " 'Room-darkeningshades',\n",
       " 'Safetycard',\n",
       " 'Selfcheck-in',\n",
       " 'Shampoo',\n",
       " 'Showerchair',\n",
       " 'Singlelevelhome',\n",
       " 'Ski-in/Ski-out',\n",
       " 'Smartlock',\n",
       " 'Smokedetector',\n",
       " 'Smokingallowed',\n",
       " 'Stairgates',\n",
       " 'Step-freeaccess',\n",
       " 'Stove',\n",
       " 'Suitableforevents',\n",
       " 'TV',\n",
       " 'Tablecornerguards',\n",
       " 'Washer',\n",
       " 'Washer/Dryer',\n",
       " 'Waterfront',\n",
       " 'Well-litpathtoentrance',\n",
       " 'Wheelchairaccessible',\n",
       " 'Wideclearancetobed',\n",
       " 'Wideclearancetoshower',\n",
       " 'Widedoorway',\n",
       " 'Wideentryway',\n",
       " 'Widehallwayclearance',\n",
       " 'Wifi',\n",
       " 'Windowguards',\n",
       " 'toilet',\n",
       " 'translationmissing:en.hosting_amenity_49',\n",
       " 'translationmissing:en.hosting_amenity_50']"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#listing the final dataset's features\n",
    "print(df_model.shape)\n",
    "list(df_model.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 4725 samples.\n",
      "Testing set has 1182 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning:\n",
      "\n",
      "Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#splitting into explanatory and response variables\n",
    "X = df_model.drop(['price', 'id', 'cleaning_fee'], axis=1)\n",
    "X = sm.add_constant(X)\n",
    "y = df_model['price']\n",
    "\n",
    "#splitting the dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size = 0.20, \n",
    "                                                    random_state = 42)\n",
    "\n",
    "#showing the results of the split\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "---\n",
    "# <center>STAGE FOUR</center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "## 5. Evaluation\n",
    "\n",
    "*Source of using statsmodels(sm): Seabold, Skipper, and Josef Perktold. “Statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference. 2010.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'statsmodels.regression.linear_model.RegressionResultsWrapper'>\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.620\n",
      "Model:                            OLS   Adj. R-squared:                  0.609\n",
      "Method:                 Least Squares   F-statistic:                     52.86\n",
      "Date:                Thu, 09 May 2019   Prob (F-statistic):               0.00\n",
      "Time:                        22:51:02   Log-Likelihood:                -32648.\n",
      "No. Observations:                5907   AIC:                         6.565e+04\n",
      "Df Residuals:                    5729   BIC:                         6.684e+04\n",
      "Df Model:                         177                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================================================\n",
      "                                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "const                                              41.1200      7.639      5.383      0.000      26.145      56.095\n",
      "host_is_superhost                                   8.1308      2.291      3.549      0.000       3.640      12.622\n",
      "accommodates                                       10.2016      0.981     10.397      0.000       8.278      12.125\n",
      "bathrooms                                          18.7747      2.108      8.905      0.000      14.642      22.908\n",
      "bedrooms                                           17.7166      1.559     11.364      0.000      14.660      20.773\n",
      "beds                                               -1.6729      1.449     -1.155      0.248      -4.513       1.167\n",
      "number_of_reviews                                  -0.1059      0.018     -5.958      0.000      -0.141      -0.071\n",
      "instant_bookable                                   -1.0441      1.894     -0.551      0.582      -4.758       2.669\n",
      "neighbourhood_cleansed_Allston                    -23.8184      3.901     -6.106      0.000     -31.465     -16.172\n",
      "neighbourhood_cleansed_Back Bay                    57.0810      3.624     15.750      0.000      49.976      64.186\n",
      "neighbourhood_cleansed_Bay Village                 22.4419     10.226      2.195      0.028       2.394      42.489\n",
      "neighbourhood_cleansed_Beacon Hill                 26.7110      4.373      6.108      0.000      18.137      35.285\n",
      "neighbourhood_cleansed_Brighton                   -24.8386      3.750     -6.623      0.000     -32.191     -17.486\n",
      "neighbourhood_cleansed_Charlestown                 23.2273      5.378      4.319      0.000      12.684      33.771\n",
      "neighbourhood_cleansed_Chinatown                   31.6667      5.749      5.508      0.000      20.397      42.937\n",
      "neighbourhood_cleansed_Dorchester                 -26.9617      3.509     -7.684      0.000     -33.841     -20.083\n",
      "neighbourhood_cleansed_Downtown                    31.4929      3.772      8.349      0.000      24.098      38.888\n",
      "neighbourhood_cleansed_East Boston                -19.5334      3.949     -4.946      0.000     -27.275     -11.791\n",
      "neighbourhood_cleansed_Fenway                      16.2577      3.677      4.421      0.000       9.049      23.467\n",
      "neighbourhood_cleansed_Hyde Park                  -44.8124      8.293     -5.403      0.000     -61.070     -28.555\n",
      "neighbourhood_cleansed_Jamaica Plain              -12.6315      3.384     -3.733      0.000     -19.265      -5.998\n",
      "neighbourhood_cleansed_Leather District            51.0500     24.948      2.046      0.041       2.143      99.957\n",
      "neighbourhood_cleansed_Longwood Medical Area      -16.4789     18.183     -0.906      0.365     -52.124      19.167\n",
      "neighbourhood_cleansed_Mattapan                   -44.7050      7.549     -5.922      0.000     -59.504     -29.906\n",
      "neighbourhood_cleansed_Mission Hill                -5.7462      4.824     -1.191      0.234     -15.202       3.710\n",
      "neighbourhood_cleansed_North End                    7.7663      4.562      1.702      0.089      -1.177      16.710\n",
      "neighbourhood_cleansed_Roslindale                 -40.1441      6.083     -6.600      0.000     -52.069     -28.220\n",
      "neighbourhood_cleansed_Roxbury                    -14.5195      4.043     -3.591      0.000     -22.445      -6.594\n",
      "neighbourhood_cleansed_South Boston                20.6289      3.880      5.317      0.000      13.022      28.235\n",
      "neighbourhood_cleansed_South Boston Waterfront     35.0167      7.808      4.485      0.000      19.711      50.322\n",
      "neighbourhood_cleansed_South End                   28.4684      3.553      8.012      0.000      21.502      35.434\n",
      "neighbourhood_cleansed_West End                     8.8566      6.084      1.456      0.146      -3.071      20.784\n",
      "neighbourhood_cleansed_West Roxbury               -45.3555      7.856     -5.773      0.000     -60.756     -29.955\n",
      "property_type_Aparthotel                           29.1468     59.760      0.488      0.626     -88.005     146.298\n",
      "property_type_Apartment                           -28.5259      7.233     -3.944      0.000     -42.706     -14.346\n",
      "property_type_Barn                                 63.4438     59.541      1.066      0.287     -53.279     180.167\n",
      "property_type_Bed and breakfast                    12.0850     11.825      1.022      0.307     -11.096      35.266\n",
      "property_type_Boat                                 14.2609     18.926      0.754      0.451     -22.842      51.363\n",
      "property_type_Boutique hotel                       -0.6164     16.213     -0.038      0.970     -32.399      31.166\n",
      "property_type_Bungalow                             72.9987     32.696      2.233      0.026       8.903     137.095\n",
      "property_type_Camper/RV                           -23.0698     42.975     -0.537      0.591    -107.317      61.177\n",
      "property_type_Chalet                              -37.4532     59.707     -0.627      0.531    -154.502      79.596\n",
      "property_type_Condominium                         -32.3600      7.651     -4.229      0.000     -47.359     -17.361\n",
      "property_type_Guest suite                         -38.7429     10.307     -3.759      0.000     -58.948     -18.538\n",
      "property_type_Guesthouse                          -33.4160     21.135     -1.581      0.114     -74.849       8.017\n",
      "property_type_Hotel                                20.1201     27.990      0.719      0.472     -34.751      74.991\n",
      "property_type_House                               -25.1252      7.501     -3.349      0.001     -39.831     -10.419\n",
      "property_type_Loft                                -22.6540     11.364     -1.994      0.046     -44.931      -0.377\n",
      "property_type_Other                               -11.9901     11.415     -1.050      0.294     -34.367      10.387\n",
      "property_type_Resort                               -4.9160     42.736     -0.115      0.908     -88.694      78.862\n",
      "property_type_Serviced apartment                   47.7269      8.947      5.335      0.000      30.188      65.266\n",
      "property_type_Tiny house                           29.5680     59.628      0.496      0.620     -87.325     146.461\n",
      "property_type_Townhouse                           -13.5713      9.028     -1.503      0.133     -31.270       4.128\n",
      "property_type_Villa                                24.2106     23.721      1.021      0.307     -22.292      70.713\n",
      "room_type_Entire home/apt                          57.8253      3.695     15.649      0.000      50.581      65.069\n",
      "room_type_Private room                              5.3542      3.380      1.584      0.113      -1.272      11.980\n",
      "room_type_Shared room                             -22.0595      6.287     -3.509      0.000     -34.385      -9.734\n",
      "cancellation_policy_flexible                       -4.2094      4.318     -0.975      0.330     -12.674       4.255\n",
      "cancellation_policy_moderate                       -4.8046      4.360     -1.102      0.271     -13.352       3.743\n",
      "cancellation_policy_strict                         64.5585      9.012      7.164      0.000      46.893      82.225\n",
      "cancellation_policy_strict_14_with_grace_period     0.3689      4.181      0.088      0.930      -7.827       8.564\n",
      "cancellation_policy_super_strict_30               -24.6573      8.217     -3.001      0.003     -40.765      -8.549\n",
      "cancellation_policy_super_strict_60                 9.8638     19.287      0.511      0.609     -27.945      47.673\n",
      "24-hourcheck-in                                     0.5160      2.876      0.179      0.858      -5.122       6.154\n",
      "Accessible-heightbed                                5.7915      7.966      0.727      0.467      -9.825      21.408\n",
      "Accessible-heighttoilet                            -1.8596      8.101     -0.230      0.818     -17.741      14.022\n",
      "Airconditioning                                    11.2372      2.514      4.469      0.000       6.308      16.166\n",
      "BBQgrill                                            1.1633      4.534      0.257      0.798      -7.725      10.051\n",
      "Babybath                                          -25.7839      9.403     -2.742      0.006     -44.218      -7.350\n",
      "Babymonitor                                         0.7851     17.108      0.046      0.963     -32.754      34.324\n",
      "Babysitterrecommendations                          -4.6887     10.278     -0.456      0.648     -24.837      15.460\n",
      "Bathtub                                            19.6087      3.090      6.346      0.000      13.551      25.666\n",
      "Bathtubwithbathchair                              -32.8297     17.563     -1.869      0.062     -67.259       1.600\n",
      "Beachessentials                                    12.6406     10.917      1.158      0.247      -8.761      34.042\n",
      "Beachfront                                         26.1644      9.577      2.732      0.006       7.390      44.939\n",
      "Bedlinens                                         -11.8756      3.167     -3.749      0.000     -18.085      -5.666\n",
      "Breakfast                                           4.0762      3.119      1.307      0.191      -2.038      10.190\n",
      "Buildingstaff                                     -15.1799     22.708     -0.668      0.504     -59.695      29.335\n",
      "Buzzer/wirelessintercom                             4.1456      3.194      1.298      0.194      -2.116      10.407\n",
      "CableTV                                             3.2588      2.161      1.508      0.132      -0.979       7.496\n",
      "Carbonmonoxidedetector                             -3.3721      2.955     -1.141      0.254      -9.166       2.422\n",
      "Cat(s)                                            -13.3417      7.605     -1.754      0.079     -28.250       1.567\n",
      "Changingtable                                       6.7012     14.603      0.459      0.646     -21.926      35.329\n",
      "Children’sbooksandtoys                             15.0699      6.315      2.386      0.017       2.691      27.449\n",
      "Children’sdinnerware                               -8.2546      8.228     -1.003      0.316     -24.385       7.876\n",
      "Cleaningbeforecheckout                             -3.9444      5.907     -0.668      0.504     -15.524       7.635\n",
      "Coffeemaker                                        -1.9149      3.751     -0.510      0.610      -9.269       5.439\n",
      "Cookingbasics                                      -3.3220      4.222     -0.787      0.431     -11.599       4.955\n",
      "Crib                                               12.0633      7.577      1.592      0.111      -2.790      26.917\n",
      "Disabledparkingspot                                -8.9960      9.265     -0.971      0.332     -27.158       9.166\n",
      "Dishesandsilverware                               -23.0995      4.767     -4.846      0.000     -32.445     -13.754\n",
      "Dishwasher                                         11.6200      3.300      3.521      0.000       5.150      18.090\n",
      "Dog(s)                                              2.7799      7.236      0.384      0.701     -11.405      16.965\n",
      "Doorman                                            -8.9855      5.718     -1.572      0.116     -20.194       2.223\n",
      "Dryer                                              -2.5699      7.606     -0.338      0.735     -17.480      12.340\n",
      "EVcharger                                          23.0132     20.805      1.106      0.269     -17.772      63.798\n",
      "Electricprofilingbed                               80.5021     34.394      2.341      0.019      13.076     147.928\n",
      "Elevator                                            9.5402      2.920      3.267      0.001       3.816      15.264\n",
      "Essentials                                          8.3976      4.485      1.872      0.061      -0.395      17.190\n",
      "Ethernetconnection                                 15.5842      4.188      3.721      0.000       7.374      23.794\n",
      "Extrapillowsandblankets                             5.7939      3.079      1.882      0.060      -0.242      11.830\n",
      "Family/kidfriendly                                  4.7869      2.189      2.187      0.029       0.496       9.078\n",
      "Fireextinguisher                                    3.0485      2.026      1.505      0.132      -0.924       7.021\n",
      "Fireplaceguards                                    -6.2254     11.981     -0.520      0.603     -29.712      17.261\n",
      "Firmmattress                                       17.5100     29.135      0.601      0.548     -39.606      74.626\n",
      "Firstaidkit                                        -4.2692      2.254     -1.894      0.058      -8.688       0.150\n",
      "Fixedgrabbarsforshower                             -8.1380     13.821     -0.589      0.556     -35.232      18.956\n",
      "Fixedgrabbarsfortoilet                             56.2564     34.475      1.632      0.103     -11.328     123.840\n",
      "Flatpathtofrontdoor                                -1.7477      7.139     -0.245      0.807     -15.743      12.248\n",
      "Freeparkingonpremises                              -1.2746      2.620     -0.486      0.627      -6.412       3.862\n",
      "Freestreetparking                                  -7.0391      2.578     -2.730      0.006     -12.094      -1.985\n",
      "Gameconsole                                        -5.1697     10.823     -0.478      0.633     -26.387      16.047\n",
      "Gardenorbackyard                                    4.0670      3.795      1.072      0.284      -3.374      11.508\n",
      "Gym                                                12.7989      3.582      3.573      0.000       5.777      19.820\n",
      "Hairdryer                                          -3.2117      2.749     -1.168      0.243      -8.601       2.178\n",
      "Handheldshowerhead                                 -0.1874      7.387     -0.025      0.980     -14.670      14.295\n",
      "Hangers                                            -5.2090      2.955     -1.763      0.078     -11.001       0.583\n",
      "Heating                                             3.2545      5.104      0.638      0.524      -6.750      13.259\n",
      "Highchair                                          -0.7952      6.759     -0.118      0.906     -14.046      12.456\n",
      "Hostgreetsyou                                       0.0907      3.593      0.025      0.980      -6.953       7.135\n",
      "Hottub                                             12.1866      5.873      2.075      0.038       0.673      23.701\n",
      "Hotwater                                            2.1784      2.669      0.816      0.414      -3.053       7.410\n",
      "Hotwaterkettle                                    -20.4160     15.850     -1.288      0.198     -51.488      10.656\n",
      "Indoorfireplace                                    10.9353      3.086      3.543      0.000       4.885      16.985\n",
      "Internet                                           -1.7201      2.321     -0.741      0.459      -6.271       2.831\n",
      "Iron                                                4.5790      2.753      1.663      0.096      -0.818       9.976\n",
      "Keypad                                            -12.2660     22.229     -0.552      0.581     -55.843      31.311\n",
      "Kitchen                                           -12.1642      3.671     -3.313      0.001     -19.361      -4.967\n",
      "Lakeaccess                                        -12.2757     15.255     -0.805      0.421     -42.180      17.629\n",
      "Laptopfriendlyworkspace                            -2.9099      2.327     -1.250      0.211      -7.472       1.652\n",
      "Lockbox                                           -14.5483     22.269     -0.653      0.514     -58.203      29.107\n",
      "Lockonbedroomdoor                                  -5.2428      2.180     -2.405      0.016      -9.517      -0.969\n",
      "Longtermstaysallowed                                2.9800      2.468      1.207      0.227      -1.859       7.819\n",
      "Luggagedropoffallowed                               2.5273      2.678      0.944      0.345      -2.722       7.776\n",
      "Microwave                                          12.2989      5.113      2.406      0.016       2.276      22.322\n",
      "Other                                               5.6924      3.799      1.498      0.134      -1.755      13.140\n",
      "Otherpet(s)                                       -24.5313     17.898     -1.371      0.171     -59.617      10.555\n",
      "Outletcovers                                        6.3317     11.103      0.570      0.569     -15.434      28.098\n",
      "Oven                                               -8.0282      5.427     -1.479      0.139     -18.668       2.611\n",
      "Pack’nPlay/travelcrib                             -14.0503      4.016     -3.498      0.000     -21.924      -6.177\n",
      "Paidparkingoffpremises                              1.5813      2.900      0.545      0.586      -4.104       7.266\n",
      "Paidparkingonpremises                             -13.3467      3.475     -3.841      0.000     -20.159      -6.535\n",
      "Patioorbalcony                                     -3.3565      3.345     -1.003      0.316      -9.915       3.202\n",
      "Petsallowed                                         6.0075      2.700      2.225      0.026       0.715      11.300\n",
      "Petsliveonthisproperty                             10.8779      6.492      1.676      0.094      -1.849      23.605\n",
      "Pocketwifi                                        -16.2228      7.577     -2.141      0.032     -31.077      -1.369\n",
      "Pool                                               14.1840      4.221      3.360      0.001       5.908      22.460\n",
      "Privatebathroom                                   -32.0464     37.809     -0.848      0.397    -106.167      42.074\n",
      "Privateentrance                                     4.1899      2.474      1.694      0.090      -0.660       9.039\n",
      "Privatelivingroom                                  13.8947      2.786      4.987      0.000       8.432      19.357\n",
      "Refrigerator                                       -1.6129      6.016     -0.268      0.789     -13.407      10.181\n",
      "Roll-inshower                                     -17.8744     18.238     -0.980      0.327     -53.628      17.879\n",
      "Room-darkeningshades                              -28.6885      5.157     -5.563      0.000     -38.799     -18.578\n",
      "Safetycard                                         -0.5672      3.049     -0.186      0.852      -6.544       5.409\n",
      "Selfcheck-in                                        8.6903     22.292      0.390      0.697     -35.011      52.392\n",
      "Shampoo                                             5.4592      2.578      2.118      0.034       0.406      10.513\n",
      "Showerchair                                       -48.7828     24.056     -2.028      0.043     -95.942      -1.623\n",
      "Singlelevelhome                                    -6.6083      5.401     -1.224      0.221     -17.196       3.980\n",
      "Ski-in/Ski-out                                    -43.0620     11.690     -3.684      0.000     -65.978     -20.146\n",
      "Smartlock                                         -17.0710     22.284     -0.766      0.444     -60.757      26.615\n",
      "Smokedetector                                       0.5059      4.585      0.110      0.912      -8.483       9.495\n",
      "Smokingallowed                                     -8.3292      6.049     -1.377      0.169     -20.188       3.530\n",
      "Stairgates                                         22.4911     10.195      2.206      0.027       2.505      42.477\n",
      "Step-freeaccess                                   -12.5702      5.572     -2.256      0.024     -23.493      -1.648\n",
      "Stove                                              12.4910      6.099      2.048      0.041       0.534      24.448\n",
      "Suitableforevents                                  -1.6986      5.445     -0.312      0.755     -12.372       8.975\n",
      "TV                                                 10.6421      2.484      4.285      0.000       5.773      15.511\n",
      "Tablecornerguards                                 -28.6059     34.616     -0.826      0.409     -96.466      39.254\n",
      "Washer                                              7.6989      7.661      1.005      0.315      -7.320      22.718\n",
      "Washer/Dryer                                       56.7747     44.624      1.272      0.203     -30.705     144.254\n",
      "Waterfront                                         -6.3847     10.310     -0.619      0.536     -26.596      13.827\n",
      "Well-litpathtoentrance                             12.2470      4.894      2.503      0.012       2.653      21.841\n",
      "Wheelchairaccessible                                5.9885      6.515      0.919      0.358      -6.783      18.760\n",
      "Wideclearancetobed                                 -2.0394      7.686     -0.265      0.791     -17.108      13.029\n",
      "Wideclearancetoshower                              -4.8623      4.674     -1.040      0.298     -14.024       4.300\n",
      "Widedoorway                                        -8.2873      7.366     -1.125      0.261     -22.727       6.153\n",
      "Wideentryway                                        6.6891      8.063      0.830      0.407      -9.118      22.497\n",
      "Widehallwayclearance                                8.5779      6.718      1.277      0.202      -4.591      21.747\n",
      "Wifi                                                2.6391      5.914      0.446      0.655      -8.955      14.233\n",
      "Windowguards                                       13.0799     11.363      1.151      0.250      -9.197      35.356\n",
      "toilet                                             -4.8623      4.674     -1.040      0.298     -14.024       4.300\n",
      "translationmissing:en.hosting_amenity_49            1.5886      4.669      0.340      0.734      -7.565      10.742\n",
      "translationmissing:en.hosting_amenity_50            1.4295      4.259      0.336      0.737      -6.920       9.779\n",
      "==============================================================================\n",
      "Omnibus:                     1227.332   Durbin-Watson:                   1.751\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3766.293\n",
      "Skew:                           1.064   Prob(JB):                         0.00\n",
      "Kurtosis:                       6.282   Cond. No.                     1.73e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 8.58e-28. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "#predicting and obtaining a summary of the Ordinary least squares method\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "\n",
    "#printing out the statistics\n",
    "print(type(model))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Based on the Regression Results, the Adjusted R<sup>2</sup> (adjusted R<sup>2</sup> was chosen instead of R<sup>2</sup> because it accounts for the number of features) states that the listed features can explain 61% of the variability in the price for Boston Airbnb. Next, we will see which features have more weight and impact than other features do and then determine whether there is a significant relationship between the features in the model. If the p-value of each of the feature is less than 0.05, we conclude that there is sufficient evidence to say that we are 95% confident that there is a significant linear relationship between the price and the feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <font color=blue> Q8. What are the features that infleunce the price in Boston airbinb? And, can we predict the price of new listings based on a predictive model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electricprofilingbed                               80.502081\n",
       "property_type_Bungalow                             72.998709\n",
       "cancellation_policy_strict                         64.558547\n",
       "property_type_Barn                                 63.443751\n",
       "room_type_Entire home/apt                          57.825315\n",
       "neighbourhood_cleansed_Back Bay                    57.080997\n",
       "Washer/Dryer                                       56.774705\n",
       "Fixedgrabbarsfortoilet                             56.256382\n",
       "neighbourhood_cleansed_Leather District            51.050006\n",
       "Showerchair                                        48.782778\n",
       "property_type_Serviced apartment                   47.726852\n",
       "neighbourhood_cleansed_West Roxbury                45.355507\n",
       "neighbourhood_cleansed_Hyde Park                   44.812446\n",
       "neighbourhood_cleansed_Mattapan                    44.705022\n",
       "Ski-in/Ski-out                                     43.061954\n",
       "const                                              41.120013\n",
       "neighbourhood_cleansed_Roslindale                  40.144102\n",
       "property_type_Guest suite                          38.742895\n",
       "property_type_Chalet                               37.453194\n",
       "neighbourhood_cleansed_South Boston Waterfront     35.016654\n",
       "property_type_Guesthouse                           33.415968\n",
       "Bathtubwithbathchair                               32.829705\n",
       "property_type_Condominium                          32.360021\n",
       "Privatebathroom                                    32.046431\n",
       "neighbourhood_cleansed_Chinatown                   31.666661\n",
       "neighbourhood_cleansed_Downtown                    31.492873\n",
       "property_type_Tiny house                           29.567973\n",
       "property_type_Aparthotel                           29.146802\n",
       "Room-darkeningshades                               28.688498\n",
       "Tablecornerguards                                  28.605936\n",
       "                                                     ...    \n",
       "Laptopfriendlyworkspace                             2.909901\n",
       "Dog(s)                                              2.779931\n",
       "Wifi                                                2.639071\n",
       "Dryer                                               2.569853\n",
       "Luggagedropoffallowed                               2.527296\n",
       "Hotwater                                            2.178412\n",
       "Wideclearancetobed                                  2.039413\n",
       "Coffeemaker                                         1.914911\n",
       "Accessible-heighttoilet                             1.859616\n",
       "Flatpathtofrontdoor                                 1.747731\n",
       "Internet                                            1.720090\n",
       "Suitableforevents                                   1.698644\n",
       "beds                                                1.672877\n",
       "Refrigerator                                        1.612933\n",
       "translationmissing:en.hosting_amenity_49            1.588552\n",
       "Paidparkingoffpremises                              1.581336\n",
       "translationmissing:en.hosting_amenity_50            1.429468\n",
       "Freeparkingonpremises                               1.274630\n",
       "BBQgrill                                            1.163336\n",
       "instant_bookable                                    1.044147\n",
       "Highchair                                           0.795203\n",
       "Babymonitor                                         0.785142\n",
       "property_type_Boutique hotel                        0.616442\n",
       "Safetycard                                          0.567184\n",
       "24-hourcheck-in                                     0.515967\n",
       "Smokedetector                                       0.505947\n",
       "cancellation_policy_strict_14_with_grace_period     0.368900\n",
       "Handheldshowerhead                                  0.187420\n",
       "number_of_reviews                                   0.105865\n",
       "Hostgreetsyou                                       0.090662\n",
       "Length: 183, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#understanding the most influential coefficients in the model\n",
    "np.abs(model.params).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "Based on the previous investigation, we can say that these features have more impact on predicting the price for Boston Airbnb listings by either increasing or decreasing the price: \n",
    "\n",
    "1. With respect to amenities: \n",
    "            \n",
    "            - Electric Profiling Bed\n",
    "            - Stair gates\n",
    "            - Washer/Dryer\n",
    "            - Hot Water Kettle\n",
    "            - Fixed Grab Bars For Toilet\n",
    "            - Private bathroom\n",
    "            - Room-darkening shades\n",
    "            \n",
    "2. With respect to property type:\n",
    "            \n",
    "            - Bungalow\n",
    "            - Serviced apartment\n",
    "            - Tiny house\n",
    "            - Hotel\n",
    "            - House\n",
    "   \n",
    "3. With respect to the neighborhood:\n",
    "            \n",
    "            - Back Bay\n",
    "            - Leather District\n",
    "            - South Boston Waterfront\n",
    "            - Chinatown\n",
    "            - Downtown\n",
    "            - Mattapan\n",
    "            - Hyde Park\n",
    "            \n",
    "4. With respect to other rules and features:\n",
    "            \n",
    "            - cancellation policy: strict\n",
    "            - room type: Entire home/apt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    183.000000\n",
       "mean      15.719676\n",
       "std       15.629477\n",
       "min        0.090662\n",
       "25%        4.633837\n",
       "50%       11.619952\n",
       "75%       22.833570\n",
       "max       80.502081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at a summary of the coefficients\n",
    "np.abs(model.params).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Electricprofilingbed                              80.502081\n",
      "property_type_Bungalow                            72.998709\n",
      "cancellation_policy_strict                        64.558547\n",
      "property_type_Barn                                63.443751\n",
      "room_type_Entire home/apt                         57.825315\n",
      "neighbourhood_cleansed_Back Bay                   57.080997\n",
      "Washer/Dryer                                      56.774705\n",
      "Fixedgrabbarsfortoilet                            56.256382\n",
      "neighbourhood_cleansed_Leather District           51.050006\n",
      "property_type_Serviced apartment                  47.726852\n",
      "const                                             41.120013\n",
      "neighbourhood_cleansed_South Boston Waterfront    35.016654\n",
      "neighbourhood_cleansed_Chinatown                  31.666661\n",
      "neighbourhood_cleansed_Downtown                   31.492873\n",
      "property_type_Tiny house                          29.567973\n",
      "property_type_Aparthotel                          29.146802\n",
      "neighbourhood_cleansed_South End                  28.468382\n",
      "neighbourhood_cleansed_Beacon Hill                26.711018\n",
      "Beachfront                                        26.164439\n",
      "property_type_Villa                               24.210605\n",
      "neighbourhood_cleansed_Charlestown                23.227346\n",
      "EVcharger                                         23.013188\n",
      "Stairgates                                        22.491148\n",
      "neighbourhood_cleansed_Bay Village                22.441854\n",
      "neighbourhood_cleansed_South Boston               20.628887\n",
      "property_type_Hotel                               20.120097\n",
      "Hotwaterkettle                                   -20.415961\n",
      "room_type_Shared room                            -22.059452\n",
      "property_type_Loft                               -22.653953\n",
      "property_type_Camper/RV                          -23.069791\n",
      "Dishesandsilverware                              -23.099468\n",
      "neighbourhood_cleansed_Allston                   -23.818354\n",
      "Otherpet(s)                                      -24.531327\n",
      "cancellation_policy_super_strict_30              -24.657292\n",
      "neighbourhood_cleansed_Brighton                  -24.838649\n",
      "property_type_House                              -25.125219\n",
      "Babybath                                         -25.783885\n",
      "neighbourhood_cleansed_Dorchester                -26.961677\n",
      "property_type_Apartment                          -28.525897\n",
      "Tablecornerguards                                -28.605936\n",
      "Room-darkeningshades                             -28.688498\n",
      "Privatebathroom                                  -32.046431\n",
      "property_type_Condominium                        -32.360021\n",
      "Bathtubwithbathchair                             -32.829705\n",
      "property_type_Guesthouse                         -33.415968\n",
      "property_type_Chalet                             -37.453194\n",
      "property_type_Guest suite                        -38.742895\n",
      "neighbourhood_cleansed_Roslindale                -40.144102\n",
      "Ski-in/Ski-out                                   -43.061954\n",
      "neighbourhood_cleansed_Mattapan                  -44.705022\n",
      "neighbourhood_cleansed_Hyde Park                 -44.812446\n",
      "neighbourhood_cleansed_West Roxbury              -45.355507\n",
      "Showerchair                                      -48.782778\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#listing only the 25% most influential coefficients in the model\n",
    "coeffs = model.params[np.abs(model.params) >= 20]\n",
    "print(coeffs.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~salitr/506.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_coeffs = coeffs.sort_values(ascending=True)\n",
    "\n",
    "\n",
    "trace1 = go.Bar(\n",
    "    y=features_coeffs.index,\n",
    "    x=features_coeffs,\n",
    "    name='Most Influential Features',\n",
    "    orientation = 'h',\n",
    "    marker = dict(\n",
    "        color = 'rgb(158,202,225)',\n",
    "        line = dict(\n",
    "            color = 'rgb(8,48,107)',\n",
    "            width = 1.5),\n",
    "        opacity=0.6\n",
    "    ))\n",
    "\n",
    "data = [trace1]\n",
    " \n",
    "layout = go.Layout(\n",
    "    title = \"Most Influential Features\",\n",
    "    xaxis=dict(\n",
    "        title='Coefficients',\n",
    "        autorange=True),\n",
    "    yaxis=dict(automargin=True,\n",
    "               autorange=True))\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Most Influential Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const                                             7.621847e-08\n",
       "host_is_superhost                                 3.897856e-04\n",
       "accommodates                                      4.282992e-25\n",
       "bathrooms                                         7.039712e-19\n",
       "bedrooms                                          1.313636e-29\n",
       "number_of_reviews                                 2.699818e-09\n",
       "neighbourhood_cleansed_Allston                    1.087438e-09\n",
       "neighbourhood_cleansed_Back Bay                   9.484528e-55\n",
       "neighbourhood_cleansed_Bay Village                2.823893e-02\n",
       "neighbourhood_cleansed_Beacon Hill                1.078504e-09\n",
       "neighbourhood_cleansed_Brighton                   3.843618e-11\n",
       "neighbourhood_cleansed_Charlestown                1.595068e-05\n",
       "neighbourhood_cleansed_Chinatown                  3.779635e-08\n",
       "neighbourhood_cleansed_Dorchester                 1.809144e-14\n",
       "neighbourhood_cleansed_Downtown                   8.553095e-17\n",
       "neighbourhood_cleansed_East Boston                7.784663e-07\n",
       "neighbourhood_cleansed_Fenway                     1.000812e-05\n",
       "neighbourhood_cleansed_Hyde Park                  6.799441e-08\n",
       "neighbourhood_cleansed_Jamaica Plain              1.909002e-04\n",
       "neighbourhood_cleansed_Leather District           4.077632e-02\n",
       "neighbourhood_cleansed_Mattapan                   3.365571e-09\n",
       "neighbourhood_cleansed_Roslindale                 4.492223e-11\n",
       "neighbourhood_cleansed_Roxbury                    3.319267e-04\n",
       "neighbourhood_cleansed_South Boston               1.097595e-07\n",
       "neighbourhood_cleansed_South Boston Waterfront    7.434838e-06\n",
       "neighbourhood_cleansed_South End                  1.360640e-15\n",
       "neighbourhood_cleansed_West Roxbury               8.177210e-09\n",
       "property_type_Apartment                           8.120351e-05\n",
       "property_type_Bungalow                            2.560919e-02\n",
       "property_type_Condominium                         2.379940e-05\n",
       "                                                      ...     \n",
       "Bedlinens                                         1.791364e-04\n",
       "Children’sbooksandtoys                            1.704440e-02\n",
       "Dishesandsilverware                               1.295579e-06\n",
       "Dishwasher                                        4.336266e-04\n",
       "Electricprofilingbed                              1.928861e-02\n",
       "Elevator                                          1.092085e-03\n",
       "Ethernetconnection                                2.002927e-04\n",
       "Family/kidfriendly                                2.879104e-02\n",
       "Freestreetparking                                 6.349476e-03\n",
       "Gym                                               3.552727e-04\n",
       "Hottub                                            3.804150e-02\n",
       "Indoorfireplace                                   3.983112e-04\n",
       "Kitchen                                           9.272693e-04\n",
       "Lockonbedroomdoor                                 1.621016e-02\n",
       "Microwave                                         1.618103e-02\n",
       "Pack’nPlay/travelcrib                             4.717461e-04\n",
       "Paidparkingonpremises                             1.238888e-04\n",
       "Petsallowed                                       2.611645e-02\n",
       "Pocketwifi                                        3.231511e-02\n",
       "Pool                                              7.845246e-04\n",
       "Privatelivingroom                                 6.320286e-07\n",
       "Room-darkeningshades                              2.776986e-08\n",
       "Shampoo                                           3.424097e-02\n",
       "Showerchair                                       4.262085e-02\n",
       "Ski-in/Ski-out                                    2.319411e-04\n",
       "Stairgates                                        2.741625e-02\n",
       "Step-freeaccess                                   2.410292e-02\n",
       "Stove                                             4.061116e-02\n",
       "TV                                                1.858946e-05\n",
       "Well-litpathtoentrance                            1.235845e-02\n",
       "Length: 72, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#listing the features that are signficant at 95% confendance level, and has p-values less than 0.005\n",
    "pvalues = model.pvalues[model.pvalues < 0.05]\n",
    "pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[NbConvertApp] Converting notebook boston_airbnb_analysis.ipynb to html',\n",
       " '[NbConvertApp] Writing 660642 bytes to boston_airbnb_analysis.html']"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!!jupyter nbconvert *.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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