generate_higher_ratings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id='part4'> How can you  generate higher ratings? </h2>\n",
    "\n",
    "\n",
    "To answer this question I will use listing data to model and predict the ratings that would be generated by a given Seattle AirBnb listing, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pycodestyle_magic extension is already loaded. To reload it, use:\n",
      "  %reload_ext pycodestyle_magic\n"
     ]
    }
   ],
   "source": [
    "# Import necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from time import time\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "% config InlineBachend.figure_format = 'retina'\n",
    "\n",
    "# Enable pep8 code style test\n",
    "%load_ext pycodestyle_magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</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>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>https://www.airbnb.com/rooms/953595</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>none</td>\n",
       "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>none</td>\n",
       "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7421966</td>\n",
       "      <td>https://www.airbnb.com/rooms/7421966</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Queen Anne Chateau</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>278830</td>\n",
       "      <td>https://www.airbnb.com/rooms/278830</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Charming craftsman 3 bdm house</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>none</td>\n",
       "      <td>We are in the beautiful neighborhood of Queen ...</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url       scrape_id last_scraped  \\\n",
       "0   241032   https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "1   953595   https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
       "2  3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
       "3  7421966  https://www.airbnb.com/rooms/7421966  20160104002432   2016-01-04   \n",
       "4   278830   https://www.airbnb.com/rooms/278830  20160104002432   2016-01-04   \n",
       "\n",
       "                                  name  \\\n",
       "0         Stylish Queen Anne Apartment   \n",
       "1   Bright & Airy Queen Anne Apartment   \n",
       "2  New Modern House-Amazing water view   \n",
       "3                   Queen Anne Chateau   \n",
       "4       Charming craftsman 3 bdm house   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "2  New modern house built in 2013.  Spectacular s...   \n",
       "3  A charming apartment that sits atop Queen Anne...   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "2  Our house is modern, light and fresh with a wa...   \n",
       "3                                                NaN   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Make your self at home in this charming one-be...                none   \n",
       "1  Chemically sensitive? We've removed the irrita...                none   \n",
       "2  New modern house built in 2013.  Spectacular s...                none   \n",
       "3  A charming apartment that sits atop Queen Anne...                none   \n",
       "4  Cozy family craftman house in beautiful neighb...                none   \n",
       "\n",
       "                               neighborhood_overview        ...         \\\n",
       "0                                                NaN        ...          \n",
       "1  Queen Anne is a wonderful, truly functional vi...        ...          \n",
       "2  Upper Queen Anne is a charming neighborhood fu...        ...          \n",
       "3                                                NaN        ...          \n",
       "4  We are in the beautiful neighborhood of Queen ...        ...          \n",
       "\n",
       "  review_scores_value requires_license license jurisdiction_names  \\\n",
       "0                10.0                f     NaN         WASHINGTON   \n",
       "1                10.0                f     NaN         WASHINGTON   \n",
       "2                10.0                f     NaN         WASHINGTON   \n",
       "3                 NaN                f     NaN         WASHINGTON   \n",
       "4                 9.0                f     NaN         WASHINGTON   \n",
       "\n",
       "  instant_bookable cancellation_policy  require_guest_profile_picture  \\\n",
       "0                f            moderate                              f   \n",
       "1                f              strict                              t   \n",
       "2                f              strict                              f   \n",
       "3                f            flexible                              f   \n",
       "4                f              strict                              f   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              2   \n",
       "1                                t                              6   \n",
       "2                                f                              2   \n",
       "3                                f                              1   \n",
       "4                                f                              1   \n",
       "\n",
       "  reviews_per_month  \n",
       "0              4.07  \n",
       "1              1.48  \n",
       "2              1.15  \n",
       "3               NaN  \n",
       "4              0.89  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the data and see the of it\n",
    "Listings_data = pd.read_csv('listings.csv')\n",
    "\n",
    "# Display the 5 first record\n",
    "Listings_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3818, 92)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data shape\n",
    "Listings_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 92 columns):\n",
      "id                                  3818 non-null int64\n",
      "listing_url                         3818 non-null object\n",
      "scrape_id                           3818 non-null int64\n",
      "last_scraped                        3818 non-null object\n",
      "name                                3818 non-null object\n",
      "summary                             3641 non-null object\n",
      "space                               3249 non-null object\n",
      "description                         3818 non-null object\n",
      "experiences_offered                 3818 non-null object\n",
      "neighborhood_overview               2786 non-null object\n",
      "notes                               2212 non-null object\n",
      "transit                             2884 non-null object\n",
      "thumbnail_url                       3498 non-null object\n",
      "medium_url                          3498 non-null object\n",
      "picture_url                         3818 non-null object\n",
      "xl_picture_url                      3498 non-null object\n",
      "host_id                             3818 non-null int64\n",
      "host_url                            3818 non-null object\n",
      "host_name                           3816 non-null object\n",
      "host_since                          3816 non-null object\n",
      "host_location                       3810 non-null object\n",
      "host_about                          2959 non-null object\n",
      "host_response_time                  3295 non-null object\n",
      "host_response_rate                  3295 non-null object\n",
      "host_acceptance_rate                3045 non-null object\n",
      "host_is_superhost                   3816 non-null object\n",
      "host_thumbnail_url                  3816 non-null object\n",
      "host_picture_url                    3816 non-null object\n",
      "host_neighbourhood                  3518 non-null object\n",
      "host_listings_count                 3816 non-null float64\n",
      "host_total_listings_count           3816 non-null float64\n",
      "host_verifications                  3818 non-null object\n",
      "host_has_profile_pic                3816 non-null object\n",
      "host_identity_verified              3816 non-null object\n",
      "street                              3818 non-null object\n",
      "neighbourhood                       3402 non-null object\n",
      "neighbourhood_cleansed              3818 non-null object\n",
      "neighbourhood_group_cleansed        3818 non-null object\n",
      "city                                3818 non-null object\n",
      "state                               3818 non-null object\n",
      "zipcode                             3811 non-null object\n",
      "market                              3818 non-null object\n",
      "smart_location                      3818 non-null object\n",
      "country_code                        3818 non-null object\n",
      "country                             3818 non-null object\n",
      "latitude                            3818 non-null float64\n",
      "longitude                           3818 non-null float64\n",
      "is_location_exact                   3818 non-null object\n",
      "property_type                       3817 non-null object\n",
      "room_type                           3818 non-null object\n",
      "accommodates                        3818 non-null int64\n",
      "bathrooms                           3802 non-null float64\n",
      "bedrooms                            3812 non-null float64\n",
      "beds                                3817 non-null float64\n",
      "bed_type                            3818 non-null object\n",
      "amenities                           3818 non-null object\n",
      "square_feet                         97 non-null float64\n",
      "price                               3818 non-null object\n",
      "weekly_price                        2009 non-null object\n",
      "monthly_price                       1517 non-null object\n",
      "security_deposit                    1866 non-null object\n",
      "cleaning_fee                        2788 non-null object\n",
      "guests_included                     3818 non-null int64\n",
      "extra_people                        3818 non-null object\n",
      "minimum_nights                      3818 non-null int64\n",
      "maximum_nights                      3818 non-null int64\n",
      "calendar_updated                    3818 non-null object\n",
      "has_availability                    3818 non-null object\n",
      "availability_30                     3818 non-null int64\n",
      "availability_60                     3818 non-null int64\n",
      "availability_90                     3818 non-null int64\n",
      "availability_365                    3818 non-null int64\n",
      "calendar_last_scraped               3818 non-null object\n",
      "number_of_reviews                   3818 non-null int64\n",
      "first_review                        3191 non-null object\n",
      "last_review                         3191 non-null object\n",
      "review_scores_rating                3171 non-null float64\n",
      "review_scores_accuracy              3160 non-null float64\n",
      "review_scores_cleanliness           3165 non-null float64\n",
      "review_scores_checkin               3160 non-null float64\n",
      "review_scores_communication         3167 non-null float64\n",
      "review_scores_location              3163 non-null float64\n",
      "review_scores_value                 3162 non-null float64\n",
      "requires_license                    3818 non-null object\n",
      "license                             0 non-null float64\n",
      "jurisdiction_names                  3818 non-null object\n",
      "instant_bookable                    3818 non-null object\n",
      "cancellation_policy                 3818 non-null object\n",
      "require_guest_profile_picture       3818 non-null object\n",
      "require_guest_phone_verification    3818 non-null object\n",
      "calculated_host_listings_count      3818 non-null int64\n",
      "reviews_per_month                   3191 non-null float64\n",
      "dtypes: float64(17), int64(13), object(62)\n",
      "memory usage: 29.3 MB\n"
     ]
    }
   ],
   "source": [
    "# A look at the variable\n",
    "Listings_data.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables classification\n",
    "\n",
    "After a quick look at the variables of our dataset we can classif them into 5 categories:\n",
    "   - DESCRIPTIONS : name, summary, space, description.\n",
    "   \n",
    "   - HOST : host_response_time, host_response_rate, host_acceptance_rate, host_is_superhost.\n",
    "   \n",
    "   - HOME DETAILS : property_type, room_type, accomodates, bathrooms, bedrooms, beds, bed_type, amenities.\n",
    "   \n",
    "   - LISTING ELEMENTS : price, cleaning_fee, extra_people, minimum_nights, maximum_nights, instant_bookable, cancellation policy.\n",
    "   \n",
    "   - SCORES : review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 30 columns):\n",
      "name                            3818 non-null object\n",
      "summary                         3641 non-null object\n",
      "space                           3249 non-null object\n",
      "description                     3818 non-null object\n",
      "host_response_time              3295 non-null object\n",
      "host_response_rate              3295 non-null object\n",
      "host_acceptance_rate            3045 non-null object\n",
      "host_is_superhost               3816 non-null object\n",
      "neighbourhood_group_cleansed    3818 non-null object\n",
      "property_type                   3817 non-null object\n",
      "room_type                       3818 non-null object\n",
      "accommodates                    3818 non-null int64\n",
      "bathrooms                       3802 non-null float64\n",
      "bedrooms                        3812 non-null float64\n",
      "beds                            3817 non-null float64\n",
      "bed_type                        3818 non-null object\n",
      "amenities                       3818 non-null object\n",
      "price                           3818 non-null object\n",
      "cleaning_fee                    2788 non-null object\n",
      "minimum_nights                  3818 non-null int64\n",
      "maximum_nights                  3818 non-null int64\n",
      "instant_bookable                3818 non-null object\n",
      "cancellation_policy             3818 non-null object\n",
      "review_scores_rating            3171 non-null float64\n",
      "review_scores_accuracy          3160 non-null float64\n",
      "review_scores_cleanliness       3165 non-null float64\n",
      "review_scores_checkin           3160 non-null float64\n",
      "review_scores_communication     3167 non-null float64\n",
      "review_scores_location          3163 non-null float64\n",
      "review_scores_value             3162 non-null float64\n",
      "dtypes: float64(10), int64(3), object(17)\n",
      "memory usage: 11.2 MB\n"
     ]
    }
   ],
   "source": [
    "list_var = ['name', 'summary', 'space', 'description', 'host_response_time',\n",
    "            'host_response_rate', 'host_acceptance_rate', 'host_is_superhost',\n",
    "            'neighbourhood_group_cleansed', 'property_type', 'room_type',\n",
    "            'accommodates', 'bathrooms', 'bedrooms', 'beds', 'bed_type',\n",
    "            'amenities', 'price', 'cleaning_fee', 'minimum_nights',\n",
    "            'maximum_nights', 'instant_bookable',  'cancellation_policy',\n",
    "            'review_scores_rating', 'review_scores_accuracy',\n",
    "            'review_scores_cleanliness', 'review_scores_checkin',\n",
    "            'review_scores_communication', 'review_scores_location',\n",
    "            'review_scores_value']\n",
    "# Get a subset of data\n",
    "df = Listings_data[list_var]\n",
    "df.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>96%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>98%</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>67%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queen Anne Chateau</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charming craftsman 3 bdm house</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name  \\\n",
       "0         Stylish Queen Anne Apartment   \n",
       "1   Bright & Airy Queen Anne Apartment   \n",
       "2  New Modern House-Amazing water view   \n",
       "3                   Queen Anne Chateau   \n",
       "4       Charming craftsman 3 bdm house   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "2  New modern house built in 2013.  Spectacular s...   \n",
       "3  A charming apartment that sits atop Queen Anne...   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "2  Our house is modern, light and fresh with a wa...   \n",
       "3                                                NaN   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                         description  host_response_time  \\\n",
       "0  Make your self at home in this charming one-be...  within a few hours   \n",
       "1  Chemically sensitive? We've removed the irrita...      within an hour   \n",
       "2  New modern house built in 2013.  Spectacular s...  within a few hours   \n",
       "3  A charming apartment that sits atop Queen Anne...                 NaN   \n",
       "4  Cozy family craftman house in beautiful neighb...      within an hour   \n",
       "\n",
       "  host_response_rate host_acceptance_rate host_is_superhost  \\\n",
       "0                96%                 100%                 f   \n",
       "1                98%                 100%                 t   \n",
       "2                67%                 100%                 f   \n",
       "3                NaN                  NaN                 f   \n",
       "4               100%                  NaN                 f   \n",
       "\n",
       "  neighbourhood_group_cleansed property_type  \n",
       "0                   Queen Anne     Apartment  \n",
       "1                   Queen Anne     Apartment  \n",
       "2                   Queen Anne         House  \n",
       "3                   Queen Anne     Apartment  \n",
       "4                   Queen Anne         House  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the 10 first variable\n",
    "df.iloc[:5, 0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>price</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>instant_bookable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...</td>\n",
       "      <td>$150.00</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>11</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>$975.00</td>\n",
       "      <td>$300.00</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...</td>\n",
       "      <td>$450.00</td>\n",
       "      <td>$125.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         room_type  accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "0  Entire home/apt             4        1.0       1.0   1.0  Real Bed   \n",
       "1  Entire home/apt             4        1.0       1.0   1.0  Real Bed   \n",
       "2  Entire home/apt            11        4.5       5.0   7.0  Real Bed   \n",
       "3  Entire home/apt             3        1.0       0.0   2.0  Real Bed   \n",
       "4  Entire home/apt             6        2.0       3.0   3.0  Real Bed   \n",
       "\n",
       "                                           amenities    price cleaning_fee  \\\n",
       "0  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...   $85.00          NaN   \n",
       "1  {TV,Internet,\"Wireless Internet\",Kitchen,\"Free...  $150.00       $40.00   \n",
       "2  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...  $975.00      $300.00   \n",
       "3  {Internet,\"Wireless Internet\",Kitchen,\"Indoor ...  $100.00          NaN   \n",
       "4  {TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...  $450.00      $125.00   \n",
       "\n",
       "   minimum_nights  maximum_nights instant_bookable  \n",
       "0               1             365                f  \n",
       "1               2              90                f  \n",
       "2               4              30                f  \n",
       "3               1            1125                f  \n",
       "4               1            1125                f  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the 12 second variable\n",
    "df.iloc[:5, 10:22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>moderate</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>strict</td>\n",
       "      <td>96.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>strict</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flexible</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>strict</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cancellation_policy  review_scores_rating  review_scores_accuracy  \\\n",
       "0            moderate                  95.0                    10.0   \n",
       "1              strict                  96.0                    10.0   \n",
       "2              strict                  97.0                    10.0   \n",
       "3            flexible                   NaN                     NaN   \n",
       "4              strict                  92.0                     9.0   \n",
       "\n",
       "   review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                       10.0                   10.0   \n",
       "1                       10.0                   10.0   \n",
       "2                       10.0                   10.0   \n",
       "3                        NaN                    NaN   \n",
       "4                        9.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \n",
       "0                         10.0                     9.0                 10.0  \n",
       "1                         10.0                    10.0                 10.0  \n",
       "2                         10.0                    10.0                 10.0  \n",
       "3                          NaN                     NaN                  NaN  \n",
       "4                         10.0                     9.0                  9.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the rest of variable\n",
    "df.iloc[:5, 22:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>neighbourhood_group_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>...</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>name_length</th>\n",
       "      <th>summary_length</th>\n",
       "      <th>space_length</th>\n",
       "      <th>description_length</th>\n",
       "      <th>amenities_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>within a few hours</td>\n",
       "      <td>96%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>98%</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>within a few hours</td>\n",
       "      <td>67%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>11</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35</td>\n",
       "      <td>241.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>243.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30</td>\n",
       "      <td>184.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_response_time host_response_rate host_acceptance_rate  \\\n",
       "0  within a few hours                96%                 100%   \n",
       "1      within an hour                98%                 100%   \n",
       "2  within a few hours                67%                 100%   \n",
       "3                 NaN                NaN                  NaN   \n",
       "4      within an hour               100%                  NaN   \n",
       "\n",
       "  host_is_superhost neighbourhood_group_cleansed property_type  \\\n",
       "0                 f                   Queen Anne     Apartment   \n",
       "1                 t                   Queen Anne     Apartment   \n",
       "2                 f                   Queen Anne         House   \n",
       "3                 f                   Queen Anne     Apartment   \n",
       "4                 f                   Queen Anne         House   \n",
       "\n",
       "         room_type  accommodates  bathrooms  bedrooms        ...         \\\n",
       "0  Entire home/apt             4        1.0       1.0        ...          \n",
       "1  Entire home/apt             4        1.0       1.0        ...          \n",
       "2  Entire home/apt            11        4.5       5.0        ...          \n",
       "3  Entire home/apt             3        1.0       0.0        ...          \n",
       "4  Entire home/apt             6        2.0       3.0        ...          \n",
       "\n",
       "   review_scores_cleanliness review_scores_checkin  \\\n",
       "0                       10.0                  10.0   \n",
       "1                       10.0                  10.0   \n",
       "2                       10.0                  10.0   \n",
       "3                        NaN                   NaN   \n",
       "4                        9.0                  10.0   \n",
       "\n",
       "  review_scores_communication review_scores_location  review_scores_value  \\\n",
       "0                        10.0                    9.0                 10.0   \n",
       "1                        10.0                   10.0                 10.0   \n",
       "2                        10.0                   10.0                 10.0   \n",
       "3                         NaN                    NaN                  NaN   \n",
       "4                        10.0                    9.0                  9.0   \n",
       "\n",
       "   name_length summary_length space_length  description_length  \\\n",
       "0           28            0.0       1000.0                1000   \n",
       "1           34          249.0       1000.0                1000   \n",
       "2           35          241.0       1000.0                1000   \n",
       "3           18          243.0          0.0                 243   \n",
       "4           30          184.0        488.0                1000   \n",
       "\n",
       "   amenities_length  \n",
       "0               114  \n",
       "1               243  \n",
       "2               291  \n",
       "3               197  \n",
       "4               181  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get lengths of description elements\n",
    "df['name_length'] = df['name'].str.len()\n",
    "df['summary_length'] = df['summary'].str.len()\n",
    "df['space_length'] = df['space'].str.len()\n",
    "df['description_length'] = df['description'].str.len()\n",
    "df['amenities_length'] = df['amenities'].str.len()\n",
    "\n",
    "# Full nan values by 0\n",
    "df['name_length'].fillna(0, inplace=True)\n",
    "df['summary_length'].fillna(0, inplace=True)\n",
    "df['space_length'].fillna(0, inplace=True)\n",
    "df['description_length'].fillna(0, inplace=True)\n",
    "df['amenities_length'].fillna(0, inplace=True)\n",
    "\n",
    "# Drop original variable\n",
    "df = df.drop(['name', 'summary', 'space', 'description', 'amenities'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "within an hour        1692\n",
       "within a few hours     968\n",
       "within a day           597\n",
       "NaN                    523\n",
       "a few days or more      38\n",
       "Name: host_response_time, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count host_response_time values\n",
    "df['host_response_time'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert host_response_time to integers\n",
    "df['host_response_time'].replace('within an hour', 1, inplace=True)\n",
    "df['host_response_time'].replace('within a few hours', 2, inplace=True)\n",
    "df['host_response_time'].replace('within a day', 3, inplace=True)\n",
    "df['host_response_time'].replace('a few days or more', 4, inplace=True)\n",
    "\n",
    "# Impute missing values using forward-fill method\n",
    "df['host_response_time'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert host_response_rate and host_acceptance_rate to number between 0 and 1\n",
    "df['host_acceptance_rate'] = df['host_acceptance_rate'].replace(\n",
    "    '%', '', regex=True).astype('float64')/100.00\n",
    "df['host_response_rate'] = df['host_response_rate'].replace(\n",
    "    '%', '', regex=True).astype('float64')/100.00\n",
    "\n",
    "# Impute missing values using forward-fill method\n",
    "df['host_response_rate'].fillna(method='ffill', inplace=True)\n",
    "df['host_acceptance_rate'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f      3038\n",
       "t       778\n",
       "NaN       2\n",
       "Name: host_is_superhost, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count host_is_superhost variable values\n",
    "df['host_is_superhost'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change f/t values to binary 0/1\n",
    "df['host_is_superhost'].replace('t', 1, inplace=True)\n",
    "df['host_is_superhost'].replace('f', 0, inplace=True)\n",
    "df['instant_bookable'].replace('t', 1, inplace=True)\n",
    "df['instant_bookable'].replace('f', 0, inplace=True)\n",
    "\n",
    "# Impute missing values by mode value\n",
    "df['host_is_superhost'].fillna(df['host_is_superhost'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "House              1733\n",
       "Apartment          1708\n",
       "Townhouse           118\n",
       "Condominium          91\n",
       "Loft                 40\n",
       "Bed & Breakfast      37\n",
       "Other                22\n",
       "Cabin                21\n",
       "Bungalow             13\n",
       "Camper/RV            13\n",
       "Boat                  8\n",
       "Tent                  5\n",
       "Treehouse             3\n",
       "Dorm                  2\n",
       "Chalet                2\n",
       "Yurt                  1\n",
       "NaN                   1\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean property_type variable\n",
    "df['property_type'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean property_type category to 5 categories\n",
    "\n",
    "\n",
    "def recode_property_type(value):\n",
    "    if value not in ['House', 'Apartment', 'Touwnhouse', 'Condominium']:\n",
    "        return 'Other'\n",
    "    return value\n",
    "\n",
    "\n",
    "df['property_type'] = df['property_type'].apply(recode_property_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed         3657\n",
       "Futon              74\n",
       "Pull-out Sofa      47\n",
       "Airbed             27\n",
       "Couch              13\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check bed_type variable\n",
    "df['bed_type'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recode bed_type to binary real bed or other\n",
    "\n",
    "\n",
    "def recode_bed_type(value):\n",
    "    if value not in ['Real Bed']:\n",
    "        return 'Other'\n",
    "    return value\n",
    "\n",
    "\n",
    "df['bed_type'] = df['bed_type'].apply(recode_bed_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert cleaning_fee and price from strings to numbers\n",
    "df['price'] = df['price'].str.replace('$', '')\n",
    "df['price'] = df['price'].str.replace(',', '').astype('float64')\n",
    "df['cleaning_fee'] = df['cleaning_fee'].str.replace('$', '')\n",
    "df['cleaning_fee'] = df['cleaning_fee'].str.replace(',', '').astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values\n",
    "df['cleaning_fee'].fillna(0, inplace=True)\n",
    "df['bathrooms'].fillna(method='ffill', inplace=True)\n",
    "df['bedrooms'].fillna(method='ffill', inplace=True)\n",
    "df['beds'].fillna(method='ffill', inplace=True)\n",
    "df['host_is_superhost'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop row without outcome\n",
    "df = df[df['review_scores_rating'].isnull() == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3171 entries, 0 to 3814\n",
      "Data columns (total 30 columns):\n",
      "host_response_time              3171 non-null float64\n",
      "host_response_rate              3171 non-null float64\n",
      "host_acceptance_rate            3171 non-null float64\n",
      "host_is_superhost               3171 non-null float64\n",
      "neighbourhood_group_cleansed    3171 non-null object\n",
      "property_type                   3171 non-null object\n",
      "room_type                       3171 non-null object\n",
      "accommodates                    3171 non-null int64\n",
      "bathrooms                       3171 non-null float64\n",
      "bedrooms                        3171 non-null float64\n",
      "beds                            3171 non-null float64\n",
      "bed_type                        3171 non-null object\n",
      "price                           3171 non-null float64\n",
      "cleaning_fee                    3171 non-null float64\n",
      "minimum_nights                  3171 non-null int64\n",
      "maximum_nights                  3171 non-null int64\n",
      "instant_bookable                3171 non-null int64\n",
      "cancellation_policy             3171 non-null object\n",
      "review_scores_rating            3171 non-null float64\n",
      "review_scores_accuracy          3160 non-null float64\n",
      "review_scores_cleanliness       3165 non-null float64\n",
      "review_scores_checkin           3160 non-null float64\n",
      "review_scores_communication     3167 non-null float64\n",
      "review_scores_location          3163 non-null float64\n",
      "review_scores_value             3162 non-null float64\n",
      "name_length                     3171 non-null int64\n",
      "summary_length                  3171 non-null float64\n",
      "space_length                    3171 non-null float64\n",
      "description_length              3171 non-null int64\n",
      "amenities_length                3171 non-null int64\n",
      "dtypes: float64(18), int64(7), object(5)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "# Look at our data\n",
    "df.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3158, 25)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distributions of numeric variable\n",
    "df_num = df.select_dtypes(exclude=['object'])  # select numeric variable\n",
    "df_num = df_num.dropna(axis=0, how='any')\n",
    "# Print the shape\n",
    "df_num.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1440 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the list of numeric variables\n",
    "df_num_variables = list(df_num)\n",
    "\n",
    "# Set the number of graphs in the facet chart\n",
    "graphs = len(df_num_variables)-1\n",
    "\n",
    "# Create a list of positions for the chart\n",
    "position = []\n",
    "for i in range(8):\n",
    "    for j in range(3):\n",
    "        b = i, j\n",
    "        position.append(b)\n",
    "\n",
    "# Create base of subplot chart.. rows x columbs = graphs\n",
    "fig, axes = plt.subplots(nrows=8, ncols=3, sharey=False, sharex=False,\n",
    "                         figsize=(12, 20))\n",
    "fig.subplots_adjust(hspace=.5)\n",
    "\n",
    "# Fill in base with graphs based off of position\n",
    "for i in range(graphs):\n",
    "    sns.distplot(df_num[df_num_variables[i]], ax=axes[position[i]], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_scores_rating           1.000000\n",
       "review_scores_value            0.696534\n",
       "review_scores_cleanliness      0.642882\n",
       "review_scores_accuracy         0.621257\n",
       "review_scores_communication    0.540620\n",
       "review_scores_checkin          0.521813\n",
       "review_scores_location         0.368423\n",
       "host_is_superhost              0.240895\n",
       "amenities_length               0.156362\n",
       "host_response_rate             0.130394\n",
       "summary_length                 0.065434\n",
       "price                          0.055551\n",
       "description_length             0.048678\n",
       "bathrooms                      0.044645\n",
       "bedrooms                       0.024235\n",
       "instant_bookable               0.006071\n",
       "minimum_nights                 0.001392\n",
       "beds                          -0.000720\n",
       "cleaning_fee                  -0.004638\n",
       "name_length                   -0.007339\n",
       "accommodates                  -0.013101\n",
       "host_acceptance_rate          -0.014684\n",
       "space_length                  -0.021034\n",
       "maximum_nights                -0.031313\n",
       "host_response_time            -0.097097\n",
       "Name: review_scores_rating, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look  at Correlation between variables\n",
    "corr = df.corr()\n",
    "# Get review_scores_rating corr\n",
    "corr['review_scores_rating'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf0b6b5c18>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "corr = (corr)\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values, linewidths=.5, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratings subscore tend to correlate with the overall rating, but beyond that, biggest correlates so far with review_score_rating appear to be:\n",
    "   - host_is_superhost \n",
    "   - amenities_length\n",
    "   - host_response_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all ratings except overall review_scores_rating outcome\n",
    "df_model = df.loc[:, ('host_response_time', 'host_response_rate',\n",
    "                      'host_acceptance_rate', 'host_is_superhost',\n",
    "                      'neighbourhood_group_cleansed', 'property_type',\n",
    "                      'room_type', 'accommodates', 'bathrooms', 'bedrooms',\n",
    "                      'beds', 'bed_type', 'price', 'cleaning_fee',\n",
    "                      'minimum_nights', 'maximum_nights', 'instant_bookable',\n",
    "                      'cancellation_policy', 'review_scores_rating',\n",
    "                      'name_length', 'summary_length', 'space_length',\n",
    "                      'description_length', 'amenities_length')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</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>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>name_length</th>\n",
       "      <th>summary_length</th>\n",
       "      <th>space_length</th>\n",
       "      <th>description_length</th>\n",
       "      <th>amenities_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3171.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.661936</td>\n",
       "      <td>0.952135</td>\n",
       "      <td>0.999685</td>\n",
       "      <td>0.236834</td>\n",
       "      <td>3.384421</td>\n",
       "      <td>1.258278</td>\n",
       "      <td>1.303059</td>\n",
       "      <td>1.742353</td>\n",
       "      <td>126.802586</td>\n",
       "      <td>46.357616</td>\n",
       "      <td>2.395774</td>\n",
       "      <td>753.925576</td>\n",
       "      <td>0.170293</td>\n",
       "      <td>94.539262</td>\n",
       "      <td>30.666982</td>\n",
       "      <td>223.163671</td>\n",
       "      <td>565.145380</td>\n",
       "      <td>910.025859</td>\n",
       "      <td>201.462630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.794221</td>\n",
       "      <td>0.113791</td>\n",
       "      <td>0.017758</td>\n",
       "      <td>0.425207</td>\n",
       "      <td>1.991495</td>\n",
       "      <td>0.593463</td>\n",
       "      <td>0.874410</td>\n",
       "      <td>1.154951</td>\n",
       "      <td>90.007172</td>\n",
       "      <td>49.580513</td>\n",
       "      <td>17.850468</td>\n",
       "      <td>1836.189972</td>\n",
       "      <td>0.375950</td>\n",
       "      <td>6.606083</td>\n",
       "      <td>5.000641</td>\n",
       "      <td>80.636162</td>\n",
       "      <td>377.050595</td>\n",
       "      <td>220.666134</td>\n",
       "      <td>69.136828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>221.500000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>446.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_response_time  host_response_rate  host_acceptance_rate  \\\n",
       "count         3171.000000         3171.000000           3171.000000   \n",
       "mean             1.661936            0.952135              0.999685   \n",
       "std              0.794221            0.113791              0.017758   \n",
       "min              1.000000            0.170000              0.000000   \n",
       "25%              1.000000            0.990000              1.000000   \n",
       "50%              1.000000            1.000000              1.000000   \n",
       "75%              2.000000            1.000000              1.000000   \n",
       "max              4.000000            1.000000              1.000000   \n",
       "\n",
       "       host_is_superhost  accommodates    bathrooms     bedrooms         beds  \\\n",
       "count        3171.000000   3171.000000  3171.000000  3171.000000  3171.000000   \n",
       "mean            0.236834      3.384421     1.258278     1.303059     1.742353   \n",
       "std             0.425207      1.991495     0.593463     0.874410     1.154951   \n",
       "min             0.000000      1.000000     0.000000     0.000000     1.000000   \n",
       "25%             0.000000      2.000000     1.000000     1.000000     1.000000   \n",
       "50%             0.000000      3.000000     1.000000     1.000000     1.000000   \n",
       "75%             0.000000      4.000000     1.000000     2.000000     2.000000   \n",
       "max             1.000000     16.000000     8.000000     7.000000    15.000000   \n",
       "\n",
       "             price  cleaning_fee  minimum_nights  maximum_nights  \\\n",
       "count  3171.000000   3171.000000     3171.000000     3171.000000   \n",
       "mean    126.802586     46.357616        2.395774      753.925576   \n",
       "std      90.007172     49.580513       17.850468     1836.189972   \n",
       "min      22.000000      0.000000        1.000000        1.000000   \n",
       "25%      75.000000      6.500000        1.000000       31.000000   \n",
       "50%     100.000000     35.000000        2.000000     1125.000000   \n",
       "75%     150.000000     70.000000        2.000000     1125.000000   \n",
       "max    1000.000000    300.000000     1000.000000   100000.000000   \n",
       "\n",
       "       instant_bookable  review_scores_rating  name_length  summary_length  \\\n",
       "count       3171.000000           3171.000000  3171.000000     3171.000000   \n",
       "mean           0.170293             94.539262    30.666982      223.163671   \n",
       "std            0.375950              6.606083     5.000641       80.636162   \n",
       "min            0.000000             20.000000     7.000000        0.000000   \n",
       "25%            0.000000             93.000000    29.000000      215.000000   \n",
       "50%            0.000000             96.000000    32.000000      242.000000   \n",
       "75%            0.000000             99.000000    34.000000      249.000000   \n",
       "max            1.000000            100.000000    36.000000     1000.000000   \n",
       "\n",
       "       space_length  description_length  amenities_length  \n",
       "count   3171.000000         3171.000000       3171.000000  \n",
       "mean     565.145380          910.025859        201.462630  \n",
       "std      377.050595          220.666134         69.136828  \n",
       "min        0.000000           34.000000          2.000000  \n",
       "25%      221.500000         1000.000000        155.000000  \n",
       "50%      549.000000         1000.000000        199.000000  \n",
       "75%     1000.000000         1000.000000        246.000000  \n",
       "max     1000.000000         1000.000000        446.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking to the range of numerical variable\n",
    "df_model.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-Encode Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- neighbourhood_group_cleansed values :  ['Queen Anne', 'Ballard', 'Other neighborhoods', 'Cascade', 'Central Area', 'University District', 'Downtown', 'Magnolia', 'West Seattle', 'Interbay', 'Beacon Hill', 'Rainier Valley', 'Delridge', 'Seward Park', 'Northgate', 'Capitol Hill', 'Lake City']\n",
      "- property_type values :  ['Apartment', 'House', 'Other', 'Condominium']\n",
      "- room_type values :  ['Entire home/apt', 'Private room', 'Shared room']\n",
      "- bed_type values :  ['Real Bed', 'Other']\n",
      "- cancellation_policy values :  ['moderate', 'strict', 'flexible']\n"
     ]
    }
   ],
   "source": [
    "# Inspect Categorical Features values\n",
    "print('- neighbourhood_group_cleansed values : ', list(df_model['neighbourhood_group_cleansed'].unique()))\n",
    "print('- property_type values : ', list(df_model['property_type'].unique()))\n",
    "print('- room_type values : ', list(df_model['room_type'].unique()))\n",
    "print('- bed_type values : ', list(df_model['bed_type'].unique()))\n",
    "print('- cancellation_policy values : ', list(df_model['cancellation_policy'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All categoricals features contain less than 4 values expet neighbourhood_group_cleansed values feature which is an important feature in our data, Therefore I decided to keep them all and change them to dummy variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical fields to dummy variables\n",
    "df_model = pd.get_dummies(df_model, columns=['neighbourhood_group_cleansed',\n",
    "                                             'property_type', 'room_type',\n",
    "                                             'bed_type',\n",
    "                                             'cancellation_policy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</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>...</th>\n",
       "      <th>property_type_House</th>\n",
       "      <th>property_type_Other</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>room_type_Shared room</th>\n",
       "      <th>bed_type_Other</th>\n",
       "      <th>bed_type_Real Bed</th>\n",
       "      <th>cancellation_policy_flexible</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_strict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   host_response_time  host_response_rate  host_acceptance_rate  \\\n",
       "0                 2.0                0.96                   1.0   \n",
       "1                 1.0                0.98                   1.0   \n",
       "2                 2.0                0.67                   1.0   \n",
       "4                 1.0                1.00                   1.0   \n",
       "5                 1.0                1.00                   1.0   \n",
       "\n",
       "   host_is_superhost  accommodates  bathrooms  bedrooms  beds  price  \\\n",
       "0                0.0             4        1.0       1.0   1.0   85.0   \n",
       "1                1.0             4        1.0       1.0   1.0  150.0   \n",
       "2                0.0            11        4.5       5.0   7.0  975.0   \n",
       "4                0.0             6        2.0       3.0   3.0  450.0   \n",
       "5                0.0             2        1.0       1.0   1.0  120.0   \n",
       "\n",
       "   cleaning_fee             ...              property_type_House  \\\n",
       "0           0.0             ...                                0   \n",
       "1          40.0             ...                                0   \n",
       "2         300.0             ...                                1   \n",
       "4         125.0             ...                                1   \n",
       "5          40.0             ...                                1   \n",
       "\n",
       "   property_type_Other  room_type_Entire home/apt  room_type_Private room  \\\n",
       "0                    0                          1                       0   \n",
       "1                    0                          1                       0   \n",
       "2                    0                          1                       0   \n",
       "4                    0                          1                       0   \n",
       "5                    0                          0                       1   \n",
       "\n",
       "   room_type_Shared room  bed_type_Other  bed_type_Real Bed  \\\n",
       "0                      0               0                  1   \n",
       "1                      0               0                  1   \n",
       "2                      0               0                  1   \n",
       "4                      0               0                  1   \n",
       "5                      0               0                  1   \n",
       "\n",
       "   cancellation_policy_flexible  cancellation_policy_moderate  \\\n",
       "0                             0                             1   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "4                             0                             0   \n",
       "5                             0                             0   \n",
       "\n",
       "   cancellation_policy_strict  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "4                           1  \n",
       "5                           1  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moodling The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data features and  labels\n",
    "review_score = df_model['review_scores_rating']\n",
    "features_final = df_model.drop(['review_scores_rating'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "features_final[features_final.columns] = scaler.fit_transform(features_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training and test sets. 80% of the data will be used for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2536 samples.\n",
      "Testing set has 635 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final,\n",
    "                                                    review_score,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "# Show 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": {},
   "source": [
    "###  Supervised Learning Models\n",
    "**The following are some of the supervised learning models  for regression problem that I will test on the data\n",
    "\n",
    "- LinearRegression\n",
    "- K-Nearest Neighbors Regression\n",
    "- Decision Tree\n",
    "- Random Forest\n",
    "- Gradient Boosted Regression\n",
    "- Support Vector Machine Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation - Creating a Training and Predicting Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "def train_predict(learner, X_train, y_train, X_test, y_test):\n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: review_score training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: review_score testing set\n",
    "    output :\n",
    "       - Root mean squared error on the testing data\n",
    "    '''\n",
    "    # Fit the learner to the training data using slicing with 'sample_size'\n",
    "    learner = learner.fit(X_train, y_train)\n",
    "    # Get the predictions on the test set,\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    # Compute accuracy on test set using accuracy_score()\n",
    "    MSE = mean_squared_error(y_test, predictions_test)\n",
    "    # Success\n",
    "    print(\"{} trained.\".format(learner.__class__.__name__))\n",
    "    # Return the results\n",
    "    return np.sqrt(MSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression trained.\n",
      "KNeighborsRegressor trained.\n",
      "DecisionTreeRegressor trained.\n",
      "SVR trained.\n",
      "RandomForestRegressor trained.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eddadsi/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor trained.\n"
     ]
    }
   ],
   "source": [
    "# Import Machine Learning Models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "\n",
    "# Initialize the  models\n",
    "reg_A = LinearRegression()\n",
    "reg_B = KNeighborsRegressor(n_neighbors=10)\n",
    "reg_C = DecisionTreeRegressor(random_state=60)\n",
    "reg_D = SVR(C=1000, gamma=0.1)\n",
    "reg_E = RandomForestRegressor(random_state=60)\n",
    "reg_F = GradientBoostingRegressor(random_state=60)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for reg in [reg_A, reg_B, reg_C, reg_D, reg_E, reg_F]:\n",
    "    reg_name = reg.__class__.__name__\n",
    "    results[reg_name] = train_predict(reg, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LinearRegression': 1209426703711.621,\n",
       " 'KNeighborsRegressor': 6.913856906447706,\n",
       " 'DecisionTreeRegressor': 8.607541099496856,\n",
       " 'SVR': 7.439640645986458,\n",
       " 'RandomForestRegressor': 6.764270793992003,\n",
       " 'GradientBoostingRegressor': 6.502552599668039}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on the run, the gradient boosting regressor performs the best followed by the random forest. I have to admit that this is not the most fair comparison because we are using mostly the default hyperparameters. Especially with the Support Vector Regressor, the hyperparameters have a significant influence on performance. (the random forest and gradient boosting methods are great for starting out because the performance is less dependent on the model settings). Nonetheless, it is not a probleme in our case because we are just looking for the most importing features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Extracting Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GradientBoostingRegressor(random_state=60)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Extract the feature importances using.\n",
    "importances = model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the seven most important features\n",
    "indices = np.argsort(importances)[::-1]\n",
    "columns = X_train.columns.values[indices[:7]]\n",
    "values = importances[indices][:7]\n",
    "# Creat the plot\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='auto')\n",
    "plt.title(\"Normalized Weights for First Seven Most Predictive Features\",\n",
    "          fontsize=20)\n",
    "plt.bar(np.arange(7), values, width=0.6, align=\"center\", color='#00A000',\n",
    "        label=\"Feature Weight\")\n",
    "plt.bar(np.arange(7)-0.3, np.cumsum(values), width=0.2, align=\"center\",\n",
    "        color='#00A0A0', label=\"Cumulative Feature Weight\")\n",
    "plt.xticks(np.arange(7), columns, rotation=45, fontsize=15)\n",
    "ax.set_xlabel('Feature', weight='normal', size=20)\n",
    "ax.set_ylabel('Weight', weight='normal', size=20)\n",
    "# Set tick label properties\n",
    "ax.tick_params('x', labelsize=15, rotation=35)\n",
    "ax.tick_params('y', labelsize=15)\n",
    "plt.legend(fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}