Airbnb (Question 2)- Part III.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ed6e1fc",
   "metadata": {},
   "source": [
    "### Boston vs. Seattle Airbnb\n",
    "\n",
    "Price Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ccf9bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d964f8b2",
   "metadata": {},
   "source": [
    "### Question 2: Is it possible to predict the price with 8 features? If yes, compare the 2 cities.\n",
    "\n",
    "### Step 1: Examining and Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a68deb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import both datasets\n",
    "seattle_listings = pd.read_csv('seattle_airbnb/listings.csv')\n",
    "boston_listings = pd.read_csv('boston_airbnb/listings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67cc9da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cleaning up the price\n",
    "seattle_listings['price'] = seattle_listings['price'].map(lambda x: int(x[1:-3].replace(',','')))\n",
    "# similair to what we did with neighbourhood, this time we will examine the overall city\n",
    "sb.boxplot(x = 'price', data = seattle_listings)\n",
    "plt.title(\"Boxplot Distribution Of Seattle Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad3cd852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# addressing and dropping the outliers using (1.5*IQR + Q3)\n",
    "Q1 = seattle_listings['price'].quantile(0.25)\n",
    "Q3 = seattle_listings['price'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "upper = 1.5*(IQR) + Q3\n",
    "\n",
    "upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "378d4947",
   "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>notes</th>\n",
       "      <th>transit</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_about</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>host_thumbnail_url</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>street</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>market</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</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",
       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a1.muscache.com/ac/pictures/67560560/c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>956883</td>\n",
       "      <td>https://www.airbnb.com/users/show/956883</td>\n",
       "      <td>Maija</td>\n",
       "      <td>2011-08-11</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>I am an artist, interior designer, and run a s...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>96%</td>\n",
       "      <td>100%</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
       "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'kba']</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Gilman Dr W, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.636289</td>\n",
       "      <td>-122.371025</td>\n",
       "      <td>t</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>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>4 weeks ago</td>\n",
       "      <td>t</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>346</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>207</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2016-01-02</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",
       "      <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>What's up with the free pillows?  Our home was...</td>\n",
       "      <td>Convenient bus stops are just down the block, ...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
       "      <td>5177328</td>\n",
       "      <td>https://www.airbnb.com/users/show/5177328</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>2013-02-21</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>Living east coast/left coast/overseas.  Time i...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>98%</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
       "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>['email', 'phone', 'facebook', 'linkedin', 're...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>7th Avenue West, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.639123</td>\n",
       "      <td>-122.365666</td>\n",
       "      <td>t</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>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>$1,000.00</td>\n",
       "      <td>$3,000.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>291</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>43</td>\n",
       "      <td>2013-08-19</td>\n",
       "      <td>2015-12-29</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",
       "      <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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/94146944/6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9851441</td>\n",
       "      <td>https://www.airbnb.com/users/show/9851441</td>\n",
       "      <td>Emily</td>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>Seattle, Washington, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a2.muscache.com/ac/users/9851441/profi...</td>\n",
       "      <td>https://a2.muscache.com/ac/users/9851441/profi...</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>8th Avenue West, Seattle, WA 98119, United States</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98119</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>47.638473</td>\n",
       "      <td>-122.369279</td>\n",
       "      <td>t</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>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>$650.00</td>\n",
       "      <td>$2,300.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>6 months ago</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>0</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",
       "      <td>NaN</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",
       "  </tbody>\n",
       "</table>\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",
       "3  7421966  https://www.airbnb.com/rooms/7421966  20160104002432   2016-01-04   \n",
       "\n",
       "                                 name  \\\n",
       "0        Stylish Queen Anne Apartment   \n",
       "1  Bright & Airy Queen Anne Apartment   \n",
       "3                  Queen Anne Chateau   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "3  A charming apartment that sits atop Queen Anne...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "3                                                NaN   \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",
       "3  A charming apartment that sits atop Queen Anne...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0                                                NaN   \n",
       "1  Queen Anne is a wonderful, truly functional vi...   \n",
       "3                                                NaN   \n",
       "\n",
       "                                               notes  \\\n",
       "0                                                NaN   \n",
       "1  What's up with the free pillows?  Our home was...   \n",
       "3                                                NaN   \n",
       "\n",
       "                                             transit  \\\n",
       "0                                                NaN   \n",
       "1  Convenient bus stops are just down the block, ...   \n",
       "3                                                NaN   \n",
       "\n",
       "                                       thumbnail_url  \\\n",
       "0                                                NaN   \n",
       "1  https://a0.muscache.com/ac/pictures/14409893/f...   \n",
       "3                                                NaN   \n",
       "\n",
       "                                          medium_url  \\\n",
       "0                                                NaN   \n",
       "1  https://a0.muscache.com/im/pictures/14409893/f...   \n",
       "3                                                NaN   \n",
       "\n",
       "                                         picture_url  \\\n",
       "0  https://a1.muscache.com/ac/pictures/67560560/c...   \n",
       "1  https://a0.muscache.com/ac/pictures/14409893/f...   \n",
       "3  https://a0.muscache.com/ac/pictures/94146944/6...   \n",
       "\n",
       "                                      xl_picture_url  host_id  \\\n",
       "0                                                NaN   956883   \n",
       "1  https://a0.muscache.com/ac/pictures/14409893/f...  5177328   \n",
       "3                                                NaN  9851441   \n",
       "\n",
       "                                    host_url host_name  host_since  \\\n",
       "0   https://www.airbnb.com/users/show/956883     Maija  2011-08-11   \n",
       "1  https://www.airbnb.com/users/show/5177328    Andrea  2013-02-21   \n",
       "3  https://www.airbnb.com/users/show/9851441     Emily  2013-11-06   \n",
       "\n",
       "                        host_location  \\\n",
       "0  Seattle, Washington, United States   \n",
       "1  Seattle, Washington, United States   \n",
       "3  Seattle, Washington, United States   \n",
       "\n",
       "                                          host_about  host_response_time  \\\n",
       "0  I am an artist, interior designer, and run a s...  within a few hours   \n",
       "1  Living east coast/left coast/overseas.  Time i...      within an hour   \n",
       "3                                                NaN                 NaN   \n",
       "\n",
       "  host_response_rate host_acceptance_rate host_is_superhost  \\\n",
       "0                96%                 100%                 f   \n",
       "1                98%                 100%                 t   \n",
       "3                NaN                  NaN                 f   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/ac/users/956883/profil...   \n",
       "1  https://a0.muscache.com/ac/users/5177328/profi...   \n",
       "3  https://a2.muscache.com/ac/users/9851441/profi...   \n",
       "\n",
       "                                    host_picture_url host_neighbourhood  \\\n",
       "0  https://a0.muscache.com/ac/users/956883/profil...         Queen Anne   \n",
       "1  https://a0.muscache.com/ac/users/5177328/profi...         Queen Anne   \n",
       "3  https://a2.muscache.com/ac/users/9851441/profi...         Queen Anne   \n",
       "\n",
       "   host_listings_count  host_total_listings_count  \\\n",
       "0                  3.0                        3.0   \n",
       "1                  6.0                        6.0   \n",
       "3                  1.0                        1.0   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0               ['email', 'phone', 'reviews', 'kba']                    t   \n",
       "1  ['email', 'phone', 'facebook', 'linkedin', 're...                    t   \n",
       "3  ['email', 'phone', 'facebook', 'reviews', 'jum...                    t   \n",
       "\n",
       "  host_identity_verified                                             street  \\\n",
       "0                      t      Gilman Dr W, Seattle, WA 98119, United States   \n",
       "1                      t  7th Avenue West, Seattle, WA 98119, United States   \n",
       "3                      t  8th Avenue West, Seattle, WA 98119, United States   \n",
       "\n",
       "  neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed     city  \\\n",
       "0    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
       "1    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
       "3    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
       "\n",
       "  state zipcode   market smart_location country_code        country  \\\n",
       "0    WA   98119  Seattle    Seattle, WA           US  United States   \n",
       "1    WA   98119  Seattle    Seattle, WA           US  United States   \n",
       "3    WA   98119  Seattle    Seattle, WA           US  United States   \n",
       "\n",
       "    latitude   longitude is_location_exact property_type        room_type  \\\n",
       "0  47.636289 -122.371025                 t     Apartment  Entire home/apt   \n",
       "1  47.639123 -122.365666                 t     Apartment  Entire home/apt   \n",
       "3  47.638473 -122.369279                 t     Apartment  Entire home/apt   \n",
       "\n",
       "   accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "0             4        1.0       1.0   1.0  Real Bed   \n",
       "1             4        1.0       1.0   1.0  Real Bed   \n",
       "3             3        1.0       0.0   2.0  Real Bed   \n",
       "\n",
       "                                           amenities  square_feet  price  \\\n",
       "0  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...          NaN     85   \n",
       "1  {TV,Internet,\"Wireless Internet\",Kitchen,\"Free...          NaN    150   \n",
       "3  {Internet,\"Wireless Internet\",Kitchen,\"Indoor ...          NaN    100   \n",
       "\n",
       "  weekly_price monthly_price security_deposit cleaning_fee  guests_included  \\\n",
       "0          NaN           NaN              NaN          NaN                2   \n",
       "1    $1,000.00     $3,000.00          $100.00       $40.00                1   \n",
       "3      $650.00     $2,300.00              NaN          NaN                1   \n",
       "\n",
       "  extra_people  minimum_nights  maximum_nights calendar_updated  \\\n",
       "0        $5.00               1             365      4 weeks ago   \n",
       "1        $0.00               2              90            today   \n",
       "3        $0.00               1            1125     6 months ago   \n",
       "\n",
       "  has_availability  availability_30  availability_60  availability_90  \\\n",
       "0                t               14               41               71   \n",
       "1                t               13               13               16   \n",
       "3                t                0                0                0   \n",
       "\n",
       "   availability_365 calendar_last_scraped  number_of_reviews first_review  \\\n",
       "0               346            2016-01-04                207   2011-11-01   \n",
       "1               291            2016-01-04                 43   2013-08-19   \n",
       "3               143            2016-01-04                  0          NaN   \n",
       "\n",
       "  last_review  review_scores_rating  review_scores_accuracy  \\\n",
       "0  2016-01-02                  95.0                    10.0   \n",
       "1  2015-12-29                  96.0                    10.0   \n",
       "3         NaN                   NaN                     NaN   \n",
       "\n",
       "   review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                       10.0                   10.0   \n",
       "1                       10.0                   10.0   \n",
       "3                        NaN                    NaN   \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",
       "3                          NaN                     NaN                  NaN   \n",
       "\n",
       "  requires_license  license jurisdiction_names instant_bookable  \\\n",
       "0                f      NaN         WASHINGTON                f   \n",
       "1                f      NaN         WASHINGTON                f   \n",
       "3                f      NaN         WASHINGTON                f   \n",
       "\n",
       "  cancellation_policy require_guest_profile_picture  \\\n",
       "0            moderate                             f   \n",
       "1              strict                             t   \n",
       "3            flexible                             f   \n",
       "\n",
       "  require_guest_phone_verification  calculated_host_listings_count  \\\n",
       "0                                f                               2   \n",
       "1                                t                               6   \n",
       "3                                f                               1   \n",
       "\n",
       "   reviews_per_month  \n",
       "0               4.07  \n",
       "1               1.48  \n",
       "3                NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seattle_listings = seattle_listings[seattle_listings['price'] <= upper]\n",
    "seattle_listings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01d6aab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = seattle_listings[['neighbourhood_group_cleansed', 'host_response_time', 'host_response_rate',\n",
    "                      'bedrooms', 'bathrooms', 'property_type', 'number_of_reviews','review_scores_rating']]\n",
    "y = seattle_listings['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "772a9e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning up the columns' names\n",
    "X = X.rename(columns = lambda x: str(x.replace('_',' ').strip().title()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635fb269",
   "metadata": {},
   "source": [
    "**Splitting dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec188382",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_state help: https://scikit-learn.org/stable/glossary.html#term-random_state\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state= 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392a457c",
   "metadata": {},
   "source": [
    "### Step 2: Addressing And Handling Missing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5e4ddb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighbourhood Group Cleansed    False\n",
       "Host Response Time               True\n",
       "Host Response Rate               True\n",
       "Bedrooms                         True\n",
       "Bathrooms                        True\n",
       "Property Type                    True\n",
       "Number Of Reviews               False\n",
       "Review Scores Rating             True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for nulls\n",
    "X_train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a60bf6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighbourhood Group Cleansed    0.0000\n",
       "Host Response Time              0.1272\n",
       "Host Response Rate              0.1272\n",
       "Bedrooms                        0.0020\n",
       "Bathrooms                       0.0052\n",
       "Property Type                   0.0004\n",
       "Number Of Reviews               0.0000\n",
       "Review Scores Rating            0.1688\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after examination, it doesn't seem like there is any need to drop any of the columns\n",
    "X_train.isnull().sum()/X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "782529bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2500 entries, 922 to 3389\n",
      "Data columns (total 8 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Neighbourhood Group Cleansed  2500 non-null   object \n",
      " 1   Host Response Time            2182 non-null   object \n",
      " 2   Host Response Rate            2182 non-null   object \n",
      " 3   Bedrooms                      2495 non-null   float64\n",
      " 4   Bathrooms                     2487 non-null   float64\n",
      " 5   Property Type                 2499 non-null   object \n",
      " 6   Number Of Reviews             2500 non-null   int64  \n",
      " 7   Review Scores Rating          2078 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 175.8+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5bbb17e",
   "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>Neighbourhood Group Cleansed</th>\n",
       "      <th>Host Response Time</th>\n",
       "      <th>Host Response Rate</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Number Of Reviews</th>\n",
       "      <th>Review Scores Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>Central Area</td>\n",
       "      <td>within a day</td>\n",
       "      <td>100%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>House</td>\n",
       "      <td>10</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Downtown</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3691</th>\n",
       "      <td>Other neighborhoods</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>100%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>House</td>\n",
       "      <td>58</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>West Seattle</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>27</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>within a day</td>\n",
       "      <td>100%</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>99</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Neighbourhood Group Cleansed  Host Response Time Host Response Rate  \\\n",
       "922                  Central Area        within a day               100%   \n",
       "1671                     Downtown      within an hour               100%   \n",
       "3691          Other neighborhoods  within a few hours               100%   \n",
       "1878                 West Seattle      within an hour               100%   \n",
       "2088                  Beacon Hill        within a day               100%   \n",
       "\n",
       "      Bedrooms  Bathrooms Property Type  Number Of Reviews  \\\n",
       "922        1.0        1.0         House                 10   \n",
       "1671       1.0        1.0     Apartment                  3   \n",
       "3691       1.0        1.0         House                 58   \n",
       "1878       1.0        1.0     Townhouse                 27   \n",
       "2088       2.0        1.0     Apartment                 99   \n",
       "\n",
       "      Review Scores Rating  \n",
       "922                   96.0  \n",
       "1671                 100.0  \n",
       "3691                  94.0  \n",
       "1878                  96.0  \n",
       "2088                  97.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the head to make imputation decisions\n",
    "# For numeric variables, I will be replacing them with the mean\n",
    "# For categorical variables, it makes more sense to replace them with the mode\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "286f3a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace categorical nulls with mode in X_train and X_test\n",
    "X_train['Host Response Time'] = X_train.loc[:,'Host Response Time'].fillna(X_train['Host Response Time'].mode()[0])\n",
    "X_train['Host Response Rate'] = X_train.loc[:,'Host Response Rate'].fillna(X_train['Host Response Rate'].mode()[0])\n",
    "X_train['Property Type'] = X_train.loc[:,'Property Type'].fillna(X_train['Property Type'].mode()[0])\n",
    "\n",
    "X_test['Host Response Time'] = X_test.loc[:,'Host Response Time'].fillna(X_test['Host Response Time'].mode()[0])\n",
    "X_test['Host Response Rate'] = X_test.loc[:,'Host Response Rate'].fillna(X_test['Host Response Rate'].mode()[0])\n",
    "X_test['Property Type'] = X_test.loc[:,'Property Type'].fillna(X_test['Property Type'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca9c6158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace numeric nulls with mean in X_train and X_test\n",
    "X_train['Bedrooms'] = X_train.loc[:,'Bedrooms'].fillna(X_train['Bedrooms'].mean())\n",
    "X_train['Bathrooms'] = X_train.loc[:,'Bathrooms'].fillna(X_train['Bathrooms'].mean())\n",
    "X_train['Review Scores Rating'] = X_train.loc[:,'Review Scores Rating'].fillna(X_train['Review Scores Rating'].mean())\n",
    "\n",
    "X_test['Bedrooms'] = X_test.loc[:,'Bedrooms'].fillna(X_test['Bedrooms'].mean())\n",
    "X_test['Bathrooms'] = X_test.loc[:,'Bathrooms'].fillna(X_test['Bathrooms'].mean())\n",
    "X_test['Review Scores Rating'] = X_test.loc[:,'Review Scores Rating'].fillna(X_test['Review Scores Rating'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789d3586",
   "metadata": {},
   "source": [
    "### Step 3: Encoding and Predicting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd3592b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding categorical values\n",
    "# help - https://www.youtube.com/watch?v=irHhDMbw3xo&t=901s\n",
    "# help - https://stackoverflow.com/questions/64656728/using-make-column-transformer-for-categorical-variables-giving-error-during-fit\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "col_trans = make_column_transformer((OneHotEncoder(handle_unknown = 'ignore'),\n",
    "                                     ['Neighbourhood Group Cleansed','Host Response Time',\n",
    "                                      'Host Response Rate','Property Type']),\n",
    "                                    remainder='passthrough')\n",
    "X_train = col_trans.fit_transform(X_train).toarray()\n",
    "X_test = col_trans.transform(X_test).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d821f7b8",
   "metadata": {},
   "source": [
    "#### Linear Regression Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2fb47c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create linear regression object and train the model using the training sets\n",
    "lm_model = LinearRegression(normalize = True).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e4f2472",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using the testing and train set\n",
    "y_test_pred = lm_model.predict(X_test)\n",
    "y_train_pred = lm_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c14d8afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared for test data: -1.5474230108318648e+23\n",
      "R-Squared for train data: 0.4353396555494863\n"
     ]
    }
   ],
   "source": [
    "# score using model\n",
    "test_score = r2_score(y_test, y_test_pred)\n",
    "train_score = r2_score(y_train, y_train_pred)\n",
    "\n",
    "print(f\"R-Squared for test data: {test_score}\")\n",
    "print(f\"R-Squared for train data: {train_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce7d43ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.39828558893388966\n",
      "R-squared value for test data: 0.36973247235487805\n",
      "Average Missed Distance From Price: 40.914781541448406\n"
     ]
    }
   ],
   "source": [
    "# help -https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm\n",
    "# help - https://towardsdatascience.com/support-vector-machines-explained-with-python-examples-cb65e8172c85\n",
    "from sklearn.svm import SVR\n",
    "model = SVR(kernel = 'linear')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "svr_model_train = r2_score(y_train, y_pred_train)\n",
    "svr_model_test = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"R-squared value for train data: {svr_model_train}\")\n",
    "print(f\"R-squared value for test data: {svr_model_test}\")\n",
    "\n",
    "#mean_square_error help - https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/\n",
    "# more help - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f665160",
   "metadata": {},
   "source": [
    "#### Support Vector Machine Method Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6fe5cd9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of Support Vector Machine Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f014f2d",
   "metadata": {},
   "source": [
    "#### XGB Regressor Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c736dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.7822738838469437\n",
      "R-squared value for test data: 0.4570009405371742\n",
      "Average Missed Distance From Price: 37.97670746708108\n"
     ]
    }
   ],
   "source": [
    "# help - https://thinkingneuron.com/how-to-create-an-xgboost-model-for-regression-in-python/\n",
    "# help - https://stackoverflow.com/questions/58318685/how-to-hide-warnings-from-xgboost-library-in-jupyter\n",
    "# we can use the sqrt of mean_squared_error, to get the distance from  line\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "model = XGBRegressor('reg:linear',verbosity = 0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "xgb_model_train = r2_score(y_train, y_pred_train)\n",
    "xgb_model_test = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"R-squared value for train data: {xgb_model_train}\")\n",
    "print(f\"R-squared value for test data: {xgb_model_test}\")\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ef1bbc",
   "metadata": {},
   "source": [
    "#### XGB Regressor Method Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "71ff8fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of XGB Regressor Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6cc49b1",
   "metadata": {},
   "source": [
    "#### Bayesian Ridge Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f207d266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.4265742873361258\n",
      "R-squared value for test data: 0.3967437735491848\n",
      "Average Missed Distance From Price: 40.02844112055365\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "model = BayesianRidge()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "baye_model_train = r2_score(y_train, y_pred_train)\n",
    "baye_model_test = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"R-squared value for train data: {baye_model_train}\")\n",
    "print(f\"R-squared value for test data: {baye_model_test}\")\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fed364",
   "metadata": {},
   "source": [
    "#### Bayesian Ridge Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc6437b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of Bayesian Ridge Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1cd87d",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e8e6aea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.8798376527180126\n",
      "R-squared value for test data: 0.16035121943307962\n",
      "Average Missed Distance From Price: 47.224416591293746\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "rfc_model_train = r2_score(y_train, y_pred_train)\n",
    "rfc_model_test = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"R-squared value for train data: {rfc_model_train}\")\n",
    "print(f\"R-squared value for test data: {rfc_model_test}\")\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223eaa23",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca8a61da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of Random Forest Classifier Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a319b4",
   "metadata": {},
   "source": [
    "#### Gradient Boosting Regressor Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26100ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.5426674347024946\n",
      "R-squared value for test data: 0.47566460944842215\n",
      "Average Missed Distance From Price: 37.31834338125408\n"
     ]
    }
   ],
   "source": [
    "# help - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "# we will go with the deafult parameters\n",
    "\n",
    "model = GradientBoostingRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "gradient_model_train = r2_score(y_train, y_pred_train)\n",
    "gradient_model_test = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f\"R-squared value for train data: {gradient_model_train}\")\n",
    "print(f\"R-squared value for test data: {gradient_model_test}\")\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86368f0",
   "metadata": {},
   "source": [
    "#### Gradient Boosting Regressor Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fadfcce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of Gradient Boosting Regressor Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136959ba",
   "metadata": {},
   "source": [
    "#### Cat Boost Regressor Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a29a24d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.047321\n",
      "0:\tlearn: 50.5058642\ttotal: 54.3ms\tremaining: 54.2s\n",
      "1:\tlearn: 49.6381421\ttotal: 55.7ms\tremaining: 27.8s\n",
      "2:\tlearn: 48.8384827\ttotal: 56.8ms\tremaining: 18.9s\n",
      "3:\tlearn: 48.1009007\ttotal: 57.9ms\tremaining: 14.4s\n",
      "4:\tlearn: 47.3968857\ttotal: 58.9ms\tremaining: 11.7s\n",
      "5:\tlearn: 46.7251168\ttotal: 60ms\tremaining: 9.94s\n",
      "6:\tlearn: 46.1580852\ttotal: 61ms\tremaining: 8.65s\n",
      "7:\tlearn: 45.5826276\ttotal: 61.6ms\tremaining: 7.63s\n",
      "8:\tlearn: 45.0794893\ttotal: 62.6ms\tremaining: 6.89s\n",
      "9:\tlearn: 44.5680349\ttotal: 63.4ms\tremaining: 6.28s\n",
      "10:\tlearn: 44.1039180\ttotal: 64.5ms\tremaining: 5.8s\n",
      "11:\tlearn: 43.6545201\ttotal: 65.4ms\tremaining: 5.38s\n",
      "12:\tlearn: 43.2791719\ttotal: 66.4ms\tremaining: 5.04s\n",
      "13:\tlearn: 42.9242935\ttotal: 67.3ms\tremaining: 4.74s\n",
      "14:\tlearn: 42.5356967\ttotal: 68.2ms\tremaining: 4.48s\n",
      "15:\tlearn: 42.1884845\ttotal: 69ms\tremaining: 4.24s\n",
      "16:\tlearn: 41.8623724\ttotal: 69.8ms\tremaining: 4.04s\n",
      "17:\tlearn: 41.5787551\ttotal: 70.7ms\tremaining: 3.85s\n",
      "18:\tlearn: 41.3125579\ttotal: 71.4ms\tremaining: 3.69s\n",
      "19:\tlearn: 41.0502523\ttotal: 72.1ms\tremaining: 3.53s\n",
      "20:\tlearn: 40.8505795\ttotal: 72.8ms\tremaining: 3.4s\n",
      "21:\tlearn: 40.6193699\ttotal: 73.6ms\tremaining: 3.27s\n",
      "22:\tlearn: 40.4680229\ttotal: 74.2ms\tremaining: 3.15s\n",
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      "24:\tlearn: 40.1112679\ttotal: 75.6ms\tremaining: 2.95s\n",
      "25:\tlearn: 39.9225955\ttotal: 76.4ms\tremaining: 2.86s\n",
      "26:\tlearn: 39.7683097\ttotal: 77ms\tremaining: 2.78s\n",
      "27:\tlearn: 39.6233684\ttotal: 77.8ms\tremaining: 2.7s\n",
      "28:\tlearn: 39.4807716\ttotal: 78.6ms\tremaining: 2.63s\n",
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      "31:\tlearn: 39.1556827\ttotal: 81.2ms\tremaining: 2.46s\n",
      "32:\tlearn: 39.0555028\ttotal: 82.1ms\tremaining: 2.4s\n",
      "33:\tlearn: 38.9760031\ttotal: 82.8ms\tremaining: 2.35s\n",
      "34:\tlearn: 38.8841091\ttotal: 83.6ms\tremaining: 2.3s\n",
      "35:\tlearn: 38.7861895\ttotal: 84.4ms\tremaining: 2.26s\n",
      "36:\tlearn: 38.6864415\ttotal: 85.2ms\tremaining: 2.22s\n",
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      "38:\tlearn: 38.4921612\ttotal: 86.7ms\tremaining: 2.13s\n",
      "39:\tlearn: 38.3973750\ttotal: 87.3ms\tremaining: 2.1s\n",
      "40:\tlearn: 38.3142943\ttotal: 88.3ms\tremaining: 2.06s\n",
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      "52:\tlearn: 37.5993915\ttotal: 97.7ms\tremaining: 1.75s\n",
      "53:\tlearn: 37.5703594\ttotal: 98.5ms\tremaining: 1.73s\n",
      "54:\tlearn: 37.5333536\ttotal: 99.3ms\tremaining: 1.71s\n",
      "55:\tlearn: 37.4986357\ttotal: 100ms\tremaining: 1.69s\n",
      "56:\tlearn: 37.4646866\ttotal: 101ms\tremaining: 1.67s\n",
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     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "\n",
    "model = CatBoostRegressor()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predicting train and test(same steps as above)\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)\n",
    "\n",
    "# r2_score\n",
    "cat_model_train = r2_score(y_train, y_pred_train)\n",
    "cat_model_test = r2_score(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12b9af9",
   "metadata": {},
   "source": [
    "#### Cat Boost Regressor Visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e85abbbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared value for train data: 0.6911675671039983\n",
      "R-squared value for test data: 0.4924641834108424\n",
      "Average Missed Distance From Price: 36.71564130927711\n"
     ]
    }
   ],
   "source": [
    "print(f\"R-squared value for train data: {cat_model_train}\")\n",
    "print(f\"R-squared value for test data: {cat_model_test}\")\n",
    "print(f\"Average Missed Distance From Price: {mean_squared_error(y_test, y_pred_test, squared = False)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dcf30464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "\n",
    "plt.scatter(y_test, y_pred_test, color = 'black')\n",
    "plt.title('Scatter Plot Of CatBoostRegressor Result')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef01f7f",
   "metadata": {},
   "source": [
    "### Step 4: R-Squared Visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "361723a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_train = [svr_model_train, xgb_model_train, baye_model_train, \n",
    "                rfc_model_train, gradient_model_train, cat_model_train]\n",
    "scores_test = [svr_model_test, xgb_model_test, baye_model_test, \n",
    "                rfc_model_test, gradient_model_test, cat_model_test]\n",
    "labels = ['Support Vector Machine', 'XGB Regressor', 'Bayesian Ridge',\n",
    "         'Random Forest Classifier', 'Gradient Boosting Regressor', 'Cat Boost Regressor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "18c6b729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [15, 8]\n",
    "\n",
    "plt.scatter(labels, scores_train, color = 'blue')\n",
    "plt.scatter(labels, scores_test, color = 'black')\n",
    "plt.legend(['scores_train', 'scores_test'])\n",
    "plt.ylabel('Score', fontsize = 15)\n",
    "plt.xlabel('Models', fontsize = 15)\n",
    "plt.title('Scores Of All Algorithm/Method Used', fontsize = 20)\n",
    "plt.savefig('Scores Of All Models Used.png');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}