8. Linear & ridge regression, decision tree, random forest, KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#https://github.com/pauljeon/airbnb-data-analysis/blob/master/analysis.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (43,61,62) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "listingsSummary = pd.read_csv(\"D:/Raghvendra/Masters/Irish Applications/DCU/DCU subjects study/Sem2_CA683 data analytics and data mining/Main Assignment/berlin airbnb 2020/listings_summary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "requiredColumns = ['price','summary', 'neighbourhood_cleansed','property_type','room_type','price','number_of_reviews',\n",
    "           'instant_bookable','review_scores_rating','beds','bedrooms','bathrooms', 'accommodates','amenities',\n",
    "           'cancellation_policy','reviews_per_month','latitude','longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25194</td>\n",
       "      <td>42358662</td>\n",
       "      <td>https://www.airbnb.com/rooms/42358662</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>Kreuzberg / Schlesisches Tor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25195</td>\n",
       "      <td>42359709</td>\n",
       "      <td>https://www.airbnb.com/rooms/42359709</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>Eastside Flat inkl. Wlan</td>\n",
       "      <td>Die Einrichtung ist in einem sauberen zustand....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Die Einrichtung ist in einem sauberen zustand....</td>\n",
       "      <td>none</td>\n",
       "      <td>Sollte es zu Komplikationen in der Wohnung geb...</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25196</td>\n",
       "      <td>42361801</td>\n",
       "      <td>https://www.airbnb.com/rooms/42361801</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>Simple and Cozy 1-Bedroom Apartment in Wedding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                            listing_url       scrape_id  \\\n",
       "25194  42358662  https://www.airbnb.com/rooms/42358662  20200218053611   \n",
       "25195  42359709  https://www.airbnb.com/rooms/42359709  20200218053611   \n",
       "25196  42361801  https://www.airbnb.com/rooms/42361801  20200218053611   \n",
       "\n",
       "      last_scraped                                            name  \\\n",
       "25194   2020-02-19                    Kreuzberg / Schlesisches Tor   \n",
       "25195   2020-02-19                        Eastside Flat inkl. Wlan   \n",
       "25196   2020-02-18  Simple and Cozy 1-Bedroom Apartment in Wedding   \n",
       "\n",
       "                                                 summary space  \\\n",
       "25194                                                NaN   NaN   \n",
       "25195  Die Einrichtung ist in einem sauberen zustand....   NaN   \n",
       "25196                                                NaN   NaN   \n",
       "\n",
       "                                             description experiences_offered  \\\n",
       "25194                                                NaN                none   \n",
       "25195  Die Einrichtung ist in einem sauberen zustand....                none   \n",
       "25196                                                NaN                none   \n",
       "\n",
       "                                   neighborhood_overview  ...  \\\n",
       "25194                                                NaN  ...   \n",
       "25195  Sollte es zu Komplikationen in der Wohnung geb...  ...   \n",
       "25196                                                NaN  ...   \n",
       "\n",
       "      instant_bookable is_business_travel_ready cancellation_policy  \\\n",
       "25194                f                        f            flexible   \n",
       "25195                t                        f            flexible   \n",
       "25196                f                        f            flexible   \n",
       "\n",
       "      require_guest_profile_picture require_guest_phone_verification  \\\n",
       "25194                             f                                f   \n",
       "25195                             f                                f   \n",
       "25196                             f                                f   \n",
       "\n",
       "       calculated_host_listings_count  \\\n",
       "25194                               1   \n",
       "25195                               1   \n",
       "25196                               1   \n",
       "\n",
       "       calculated_host_listings_count_entire_homes  \\\n",
       "25194                                            0   \n",
       "25195                                            0   \n",
       "25196                                            1   \n",
       "\n",
       "      calculated_host_listings_count_private_rooms  \\\n",
       "25194                                            1   \n",
       "25195                                            1   \n",
       "25196                                            0   \n",
       "\n",
       "       calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "25194                                            0                NaN  \n",
       "25195                                            0                NaN  \n",
       "25196                                            0                NaN  \n",
       "\n",
       "[3 rows x 106 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listingsSummary.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    25197.000000\n",
       "mean        73.849863\n",
       "std        225.236453\n",
       "min          0.000000\n",
       "25%         35.000000\n",
       "50%         50.000000\n",
       "75%         79.000000\n",
       "max       9000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data cleaning of price column\n",
    "\n",
    "priceUpdated = listingsSummary['price'].map(lambda p: int(p[1:-3].replace(\",\", \"\")))\n",
    "listingsSummary['price'] = priceUpdated\n",
    "listingsSummary['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing NaN values with 0\n",
    "listingsSummary.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# excluding listings with \"0\" for price, bedrooms, etc\n",
    "listingsSummary = listingsSummary[listingsSummary.review_scores_rating > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.beds > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.price > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.reviews_per_month > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.bathrooms > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.bedrooms > 0]\n",
    "listingsSummary = listingsSummary[listingsSummary.accommodates > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>calculated_host_listings_count_entire_homes</th>\n",
       "      <th>calculated_host_listings_count_private_rooms</th>\n",
       "      <th>calculated_host_listings_count_shared_rooms</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3176</td>\n",
       "      <td>https://www.airbnb.com/rooms/3176</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>Fabulous Flat in great Location</td>\n",
       "      <td>This beautiful first floor apartment  is situa...</td>\n",
       "      <td>1st floor (68m2) apartment on Kollwitzplatz/ P...</td>\n",
       "      <td>This beautiful first floor apartment  is situa...</td>\n",
       "      <td>none</td>\n",
       "      <td>The neighbourhood is famous for its variety of...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3309</td>\n",
       "      <td>https://www.airbnb.com/rooms/3309</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>BerlinSpot Schöneberg near KaDeWe</td>\n",
       "      <td>First of all: I prefer short-notice bookings. ...</td>\n",
       "      <td>Your room is really big and has 26 sqm, is ver...</td>\n",
       "      <td>First of all: I prefer short-notice bookings. ...</td>\n",
       "      <td>none</td>\n",
       "      <td>My flat is in the middle of West-Berlin, direc...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6883</td>\n",
       "      <td>https://www.airbnb.com/rooms/6883</td>\n",
       "      <td>20200218053611</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>Stylish East Side Loft in Center with AC &amp; 2 b...</td>\n",
       "      <td>0</td>\n",
       "      <td>Stay in a stylish loft on the second floor and...</td>\n",
       "      <td>Stay in a stylish loft on the second floor and...</td>\n",
       "      <td>none</td>\n",
       "      <td>The emerging and upcoming East of the new hip ...</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                        listing_url       scrape_id last_scraped  \\\n",
       "0  3176  https://www.airbnb.com/rooms/3176  20200218053611   2020-02-18   \n",
       "1  3309  https://www.airbnb.com/rooms/3309  20200218053611   2020-02-19   \n",
       "2  6883  https://www.airbnb.com/rooms/6883  20200218053611   2020-02-19   \n",
       "\n",
       "                                                name  \\\n",
       "0                    Fabulous Flat in great Location   \n",
       "1                  BerlinSpot Schöneberg near KaDeWe   \n",
       "2  Stylish East Side Loft in Center with AC & 2 b...   \n",
       "\n",
       "                                             summary  \\\n",
       "0  This beautiful first floor apartment  is situa...   \n",
       "1  First of all: I prefer short-notice bookings. ...   \n",
       "2                                                  0   \n",
       "\n",
       "                                               space  \\\n",
       "0  1st floor (68m2) apartment on Kollwitzplatz/ P...   \n",
       "1  Your room is really big and has 26 sqm, is ver...   \n",
       "2  Stay in a stylish loft on the second floor and...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  This beautiful first floor apartment  is situa...                none   \n",
       "1  First of all: I prefer short-notice bookings. ...                none   \n",
       "2  Stay in a stylish loft on the second floor and...                none   \n",
       "\n",
       "                               neighborhood_overview  ... instant_bookable  \\\n",
       "0  The neighbourhood is famous for its variety of...  ...                f   \n",
       "1  My flat is in the middle of West-Berlin, direc...  ...                f   \n",
       "2  The emerging and upcoming East of the new hip ...  ...                f   \n",
       "\n",
       "  is_business_travel_ready          cancellation_policy  \\\n",
       "0                        f  strict_14_with_grace_period   \n",
       "1                        f  strict_14_with_grace_period   \n",
       "2                        f                     moderate   \n",
       "\n",
       "  require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                             f                                f   \n",
       "1                             f                                f   \n",
       "2                             f                                t   \n",
       "\n",
       "   calculated_host_listings_count  \\\n",
       "0                               1   \n",
       "1                               1   \n",
       "2                               1   \n",
       "\n",
       "   calculated_host_listings_count_entire_homes  \\\n",
       "0                                            1   \n",
       "1                                            0   \n",
       "2                                            1   \n",
       "\n",
       "  calculated_host_listings_count_private_rooms  \\\n",
       "0                                            0   \n",
       "1                                            1   \n",
       "2                                            0   \n",
       "\n",
       "   calculated_host_listings_count_shared_rooms  reviews_per_month  \n",
       "0                                            0               1.12  \n",
       "1                                            0               0.34  \n",
       "2                                            0               1.08  \n",
       "\n",
       "[3 rows x 106 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listingsSummary.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on neighbourhood, let's check the frequency of properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Prenzlauer Berg Südwest': 511,\n",
       "         'Schöneberg-Nord': 397,\n",
       "         'Frankfurter Allee Süd FK': 1087,\n",
       "         'Helmholtzplatz': 346,\n",
       "         'nördliche Luisenstadt': 360,\n",
       "         'südliche Luisenstadt': 515,\n",
       "         'Brunnenstr. Süd': 637,\n",
       "         'Tempelhofer Vorstadt': 1035,\n",
       "         'Prenzlauer Berg Süd': 467,\n",
       "         'Moabit Ost': 288,\n",
       "         'Schillerpromenade': 506,\n",
       "         'Alexanderplatz': 897,\n",
       "         'Neue Kantstraße': 123,\n",
       "         'Prenzlauer Berg Nordwest': 524,\n",
       "         'Ostpreußendamm': 27,\n",
       "         'Schmöckwitz/Karolinenhof/Rauchfangswerder': 13,\n",
       "         'Neuköllner Mitte/Zentrum': 630,\n",
       "         'Frankfurter Allee Nord': 549,\n",
       "         'Kantstraße': 115,\n",
       "         'Schmargendorf': 23,\n",
       "         'Regierungsviertel': 190,\n",
       "         'Prenzlauer Berg Nord': 316,\n",
       "         'Reuterstraße': 818,\n",
       "         'Kurfürstendamm': 88,\n",
       "         'Schöneberg-Süd': 365,\n",
       "         'Blankenfelde/Niederschönhausen': 42,\n",
       "         'Südliche Friedrichstadt': 306,\n",
       "         'Moabit West': 384,\n",
       "         'Wiesbadener Straße': 43,\n",
       "         'West 3': 16,\n",
       "         'Neu Lichtenberg': 183,\n",
       "         'Blankenburg/Heinersdorf/Märchenland': 12,\n",
       "         'Friedenau': 156,\n",
       "         'Brunnenstr. Nord': 221,\n",
       "         'Ost 2': 57,\n",
       "         'Volkspark Wilmersdorf': 143,\n",
       "         'Pankow Zentrum': 97,\n",
       "         'Osloer Straße': 280,\n",
       "         'Drakestr.': 66,\n",
       "         'Prenzlauer Berg Ost': 201,\n",
       "         'Pankow Süd': 138,\n",
       "         'Halensee': 66,\n",
       "         'Karlshorst': 41,\n",
       "         'Alt-Lichtenberg': 126,\n",
       "         'Zehlendorf  Nord': 48,\n",
       "         'Düsseldorfer Straße': 132,\n",
       "         'Tiergarten Süd': 163,\n",
       "         'Rudow': 30,\n",
       "         'Mierendorffplatz': 66,\n",
       "         'Wedding Zentrum': 438,\n",
       "         'Karl-Marx-Allee-Süd': 290,\n",
       "         'Altglienicke': 14,\n",
       "         'Westend': 80,\n",
       "         'Mahlsdorf': 27,\n",
       "         'Ost 1': 46,\n",
       "         'West 5': 11,\n",
       "         'Rixdorf': 702,\n",
       "         'Biesdorf': 27,\n",
       "         'Zehlendorf  Südwest': 32,\n",
       "         'Johannisthal': 21,\n",
       "         'Karl-Marx-Allee-Nord': 243,\n",
       "         'Barstraße': 45,\n",
       "         'Otto-Suhr-Allee': 136,\n",
       "         'Schloß Charlottenburg': 113,\n",
       "         'Lichtenrade': 22,\n",
       "         'Albrechtstr.': 83,\n",
       "         'Buckow': 13,\n",
       "         'Baumschulenweg': 61,\n",
       "         'Schloßstr.': 47,\n",
       "         'Karow': 13,\n",
       "         'Alt  Treptow': 147,\n",
       "         'Gatow / Kladow': 18,\n",
       "         'Parkviertel': 271,\n",
       "         'Weißensee': 158,\n",
       "         'Alt-Hohenschönhausen Nord': 9,\n",
       "         'Britz': 72,\n",
       "         'Tempelhof': 219,\n",
       "         'Nord 2': 14,\n",
       "         'Hellersdorf-Nord': 4,\n",
       "         'Marzahn-Mitte': 4,\n",
       "         'MV 1': 12,\n",
       "         'Plänterwald': 47,\n",
       "         'Teltower Damm': 35,\n",
       "         'Marienfelde': 5,\n",
       "         'Nord 1': 26,\n",
       "         'Adlershof': 22,\n",
       "         'Friedrichshagen': 12,\n",
       "         'Heerstrasse': 33,\n",
       "         'Schönholz/Wilhelmsruh/Rosenthal': 23,\n",
       "         'Rummelsburger Bucht': 26,\n",
       "         'Friedrichsfelde Nord': 32,\n",
       "         'Buchholz': 23,\n",
       "         'Grunewald': 48,\n",
       "         'West 4': 21,\n",
       "         'Rahnsdorf/Hessenwinkel': 27,\n",
       "         'Köpenick-Nord': 14,\n",
       "         'Frankfurter Allee Süd': 24,\n",
       "         'MV 2': 5,\n",
       "         'Marzahn-Süd': 26,\n",
       "         'Bohnsdorf': 6,\n",
       "         'Fennpfuhl': 38,\n",
       "         'Kaulsdorf': 11,\n",
       "         'Mariendorf': 40,\n",
       "         'Charlottenburg Nord': 14,\n",
       "         'Gropiusstadt': 7,\n",
       "         'Siemensstadt': 6,\n",
       "         'Oberschöneweide': 42,\n",
       "         'Buch': 8,\n",
       "         'Niederschöneweide': 19,\n",
       "         'Köllnische Heide': 34,\n",
       "         'Weißensee Ost': 26,\n",
       "         'Grünau': 10,\n",
       "         'Falkenhagener Feld': 7,\n",
       "         'Hakenfelde': 13,\n",
       "         'West 1': 19,\n",
       "         'Hellersdorf-Süd': 4,\n",
       "         'Spandau Mitte': 16,\n",
       "         'Alt-Hohenschönhausen Süd': 26,\n",
       "         'Haselhorst': 6,\n",
       "         'West 2': 11,\n",
       "         'Altstadt-Kietz': 5,\n",
       "         'Wilhelmstadt': 23,\n",
       "         'Dammvorstadt': 18,\n",
       "         'Brunsbütteler Damm': 7,\n",
       "         'Neu-Hohenschönhausen Süd': 3,\n",
       "         'Malchow, Wartenberg und Falkenberg': 7,\n",
       "         'Lankwitz': 15,\n",
       "         'Friedrichsfelde Süd': 30,\n",
       "         'Köpenick-Süd': 1,\n",
       "         'Heerstraße Nord': 6,\n",
       "         'Müggelheim': 5,\n",
       "         'Neu-Hohenschönhausen Nord': 6,\n",
       "         'Kölln. Vorstadt/Spindlersf.': 5,\n",
       "         'Hellersdorf-Ost': 2,\n",
       "         'Allende-Viertel': 2,\n",
       "         'Forst Grunewald': 1})"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propertyDictionary = Counter(listingsSummary['neighbourhood_cleansed'])\n",
    "propertyDictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's check the average price in berlin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.71045974483062"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averagePrice = sum(listingsSummary.price) / float(len(listingsSummary.price))\n",
    "averagePrice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am curious for average price of each neighbourhood now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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</th>\n",
       "      <th>price</th>\n",
       "      <th>number of listings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adlershof</td>\n",
       "      <td>42.045455</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albrechtstr.</td>\n",
       "      <td>51.120482</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Alexanderplatz</td>\n",
       "      <td>135.375697</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Allende-Viertel</td>\n",
       "      <td>162.500000</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Alt  Treptow</td>\n",
       "      <td>54.850340</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>Wilhelmstadt</td>\n",
       "      <td>42.782609</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>Zehlendorf  Nord</td>\n",
       "      <td>71.041667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>Zehlendorf  Südwest</td>\n",
       "      <td>83.562500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>nördliche Luisenstadt</td>\n",
       "      <td>67.511111</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>südliche Luisenstadt</td>\n",
       "      <td>69.582524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             neighbourhood       price  number of listings\n",
       "0                Adlershof   42.045455                 511\n",
       "1             Albrechtstr.   51.120482                 397\n",
       "2           Alexanderplatz  135.375697                1087\n",
       "3          Allende-Viertel  162.500000                 346\n",
       "4             Alt  Treptow   54.850340                 360\n",
       "..                     ...         ...                 ...\n",
       "131           Wilhelmstadt   42.782609                   6\n",
       "132       Zehlendorf  Nord   71.041667                   5\n",
       "133    Zehlendorf  Südwest   83.562500                   2\n",
       "134  nördliche Luisenstadt   67.511111                   2\n",
       "135   südliche Luisenstadt   69.582524                   1\n",
       "\n",
       "[136 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the names from earlier dictionary\n",
    "namesNeighborhood = list(propertyDictionary.keys())\n",
    "\n",
    "# select necessary columns\n",
    "neighbourhoodPrices = listingsSummary[['neighbourhood_cleansed', 'price']]\n",
    "neighbourhoodPrices.columns = ['neighbourhood', 'price']\n",
    "\n",
    "# we pick out the rows which have neighborhood names with 400+ listings.\n",
    "neighbourhoodPrices = neighbourhoodPrices[neighbourhoodPrices['neighbourhood'].isin(namesNeighborhood)]\n",
    "\n",
    "# group by neighbourhood and then aggreate the prices based on mean\n",
    "groupedPrices = neighbourhoodPrices.groupby('neighbourhood')\n",
    "neighbourhoodPrices = groupedPrices['price'].agg(np.mean)\n",
    "\n",
    "# conversion of dictionary to a table\n",
    "neighbourhoodPrices = neighbourhoodPrices.reset_index()\n",
    "neighbourhoodPrices['number of listings'] = propertyDictionary.values()\n",
    "\n",
    "neighbourhoodPrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.geeksforgeeks.org/python-pandas-dataframe-sort_values-set-1/\n",
    "#sorting values to plot top neighbourhoods with maximum listings\n",
    "neighbourhoodPricesMostListings = neighbourhoodPrices.sort_values(\"number of listings\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#requiredColumns = [\"neighbourhood\", \"price\",\"number of listings\" ]\n",
    "#neighbourhoodPricesMostListingsTop = pd.DataFrame(columns=['neighbourhood','price','number of listings'])\n",
    "\n",
    "#https://www.geeksforgeeks.org/reset-index-in-pandas-dataframe/\n",
    "neighbourhoodPricesMostListings.reset_index(inplace = True)\n",
    "neighbourhoodPricesMostListingsTop = neighbourhoodPricesMostListings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>index</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>number of listings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Alexanderplatz</td>\n",
       "      <td>135.375697</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alt-Lichtenberg</td>\n",
       "      <td>49.428571</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>Baumschulenweg</td>\n",
       "      <td>44.508197</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    neighbourhood       price  number of listings\n",
       "0      2   Alexanderplatz  135.375697                1087\n",
       "1      7  Alt-Lichtenberg   49.428571                1035\n",
       "2     11   Baumschulenweg   44.508197                 897"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhoodPricesMostListingsTop.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the neighbourhood depending on the prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x175596ba240>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://matplotlib.org/3.1.0/gallery/color/named_colors.html\n",
    "p = neighbourhoodPricesMostListingsTop.sort_values(by = 'price')\n",
    "p.plot(x = \"neighbourhood\", y = \"price\", kind='bar', color = 'turquoise', figsize =(15,7), \n",
    "       title = 'neighbourhoods with prices', legend = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see, few neighbourhoods are costlier than others. But, we should check if it's the neighbourhood that causes more price or some other factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1755b586da0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "propertyType = listingsSummary.property_type\n",
    "p = Counter(propertyType)\n",
    "\n",
    "dfProperty = pd.DataFrame.from_dict(p, orient='index').sort_values(by=0)\n",
    "dfProperty.columns = ['property_type']\n",
    "dfProperty.plot.bar(y= 'property_type', color = 'olive',fontsize = 17, legend = False, figsize= (13, 7),\n",
    "                     title = \"Types of property\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1755acf0208>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "room = listingsSummary.room_type\n",
    "r = Counter(room)\n",
    "#https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html\n",
    "DFroom = pd.DataFrame.from_dict(r, orient='index').sort_values(by=0)\n",
    "DFroom.columns = ['room_type']\n",
    "DFroom.plot.pie(y = 'room_type', colormap = 'Greens', figsize=(15,15), fontsize = 15, autopct = '%.2f',\n",
    "                 legend = False, title = 'types of room')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select property according to types, and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_type        room_type  price\n",
       "0      Apartment  Entire home/apt     90\n",
       "1      Apartment     Private room     28\n",
       "2           Loft  Entire home/apt    125\n",
       "3      Apartment     Private room     33\n",
       "4      Apartment  Entire home/apt    180\n",
       "6      Apartment  Entire home/apt     90\n",
       "8           Loft  Entire home/apt    129\n",
       "9      Apartment  Entire home/apt     70\n",
       "10     Apartment  Entire home/apt     98\n",
       "11     Apartment  Entire home/apt    160"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roomProperty = listingsSummary[['property_type', 'room_type', 'price']]\n",
    "roomProperty.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's groupby and aggregate according to price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>room_type</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Hotel room</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Aparthotel</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>122.666667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Apartment</td>\n",
       "      <td>85.873266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.140648</td>\n",
       "      <td>35.008696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Barn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.750000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bed and breakfast</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>53.541667</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boat</td>\n",
       "      <td>143.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Boutique hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525.958333</td>\n",
       "      <td>101.647059</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bungalow</td>\n",
       "      <td>79.842105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cabin</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Camper/RV</td>\n",
       "      <td>42.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Casa particular (Cuba)</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Castle</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Condominium</td>\n",
       "      <td>96.966912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.540000</td>\n",
       "      <td>28.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cottage</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dome house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guest suite</td>\n",
       "      <td>70.703704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.687500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guesthouse</td>\n",
       "      <td>76.022727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.571429</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hostel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>442.975610</td>\n",
       "      <td>210.625000</td>\n",
       "      <td>155.872340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>882.195122</td>\n",
       "      <td>80.384615</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>House</td>\n",
       "      <td>127.044118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.867299</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Houseboat</td>\n",
       "      <td>229.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lighthouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Loft</td>\n",
       "      <td>140.356364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.928571</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Other</td>\n",
       "      <td>139.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pension (South Korea)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Resort</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>97.551181</td>\n",
       "      <td>339.586957</td>\n",
       "      <td>88.222222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tiny house</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tipi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Townhouse</td>\n",
       "      <td>115.607143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.096774</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Treehouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Villa</td>\n",
       "      <td>138.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "room_type               Entire home/apt  Hotel room  Private room  Shared room\n",
       "property_type                                                                 \n",
       "Aparthotel                   118.750000  143.500000    122.666667          NaN\n",
       "Apartment                     85.873266         NaN     41.140648    35.008696\n",
       "Barn                                NaN         NaN     26.750000          NaN\n",
       "Bed and breakfast                   NaN  117.500000     53.541667    20.000000\n",
       "Boat                         143.400000         NaN           NaN    98.000000\n",
       "Boutique hotel                      NaN  525.958333    101.647059    30.000000\n",
       "Bungalow                      79.842105         NaN     29.333333          NaN\n",
       "Cabin                         75.600000         NaN           NaN          NaN\n",
       "Camper/RV                     42.666667         NaN     31.000000          NaN\n",
       "Casa particular (Cuba)        50.000000         NaN           NaN          NaN\n",
       "Castle                       140.000000         NaN           NaN          NaN\n",
       "Cave                                NaN         NaN     28.000000          NaN\n",
       "Condominium                   96.966912         NaN     38.540000    28.166667\n",
       "Cottage                       82.000000         NaN     48.000000          NaN\n",
       "Dome house                          NaN         NaN           NaN    19.000000\n",
       "Guest suite                   70.703704         NaN     37.687500          NaN\n",
       "Guesthouse                    76.022727         NaN     40.571429          NaN\n",
       "Hostel                              NaN  442.975610    210.625000   155.872340\n",
       "Hotel                               NaN  882.195122     80.384615          NaN\n",
       "House                        127.044118         NaN     37.867299    62.000000\n",
       "Houseboat                    229.416667         NaN     75.000000          NaN\n",
       "Lighthouse                          NaN         NaN     65.000000          NaN\n",
       "Loft                         140.356364         NaN     60.928571    24.500000\n",
       "Other                        139.200000         NaN     51.500000          NaN\n",
       "Pension (South Korea)               NaN         NaN    115.000000          NaN\n",
       "Resort                              NaN         NaN     27.000000          NaN\n",
       "Serviced apartment            97.551181  339.586957     88.222222          NaN\n",
       "Tent                                NaN         NaN           NaN    49.000000\n",
       "Tiny house                    55.500000         NaN     54.000000    40.000000\n",
       "Tipi                                NaN         NaN     35.000000          NaN\n",
       "Townhouse                    115.607143         NaN     47.096774    22.000000\n",
       "Treehouse                           NaN         NaN     20.000000          NaN\n",
       "Villa                        138.333333         NaN     36.200000          NaN"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propertyRoomGrouped = roomProperty.groupby(['property_type', 'room_type']).mean()\n",
    "\n",
    "# replace nan with 0\n",
    "propertyRoomGrouped.fillna(0.00, inplace=True)\n",
    "\n",
    "# resetting the index\n",
    "propertyRoomGrouped = propertyRoomGrouped.reset_index()\n",
    "\n",
    "# pivoting the table based on the 3 factors, in order.\n",
    "propertyRoomGrouped = propertyRoomGrouped.pivot('property_type', 'room_type', 'price')\n",
    "\n",
    "propertyRoomGrouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go to the data setting for Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data = listingsSummary[['price','room_type', 'accommodates','bathrooms','bedrooms','beds','review_scores_rating',\n",
    "          'instant_bookable', 'cancellation_policy', 'amenities']]\n",
    "\n",
    "\n",
    "#data = listingsSummary[['price','amenities']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cancellation policy and instant bookable factors are little bit more complicated.\n",
    "# we use pandas get_dummies function to convert the categorical variable into indicator variables\n",
    "\n",
    "cancel_policy = pd.get_dummies(data.cancellation_policy).astype(int)\n",
    "instant_booking = pd.get_dummies(data.instant_bookable, prefix = 'instant_booking').astype(int)\n",
    "room_type = pd.get_dummies(data.room_type).astype(int)\n",
    "\n",
    "# ib has 2 columns, so we can just drop one of them.\n",
    "instant_booking = instant_booking.drop('instant_booking_f', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we drop the original columns and replace them with indicator columns\n",
    "data = data.drop(['cancellation_policy', 'instant_bookable', 'room_type'], axis = 1)\n",
    "#data = data.drop([ 'instant_bookable', 'room_type'], axis = 1)\n",
    "data = pd.concat((data, cancel_policy, instant_booking, room_type), axis = 1)\n",
    "#data = pd.concat((data, instant_booking, room_type), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the amenities list to draw out how many amenities each listing has\n",
    "\n",
    "amenities_list = []\n",
    "\n",
    "for element in data.amenities:\n",
    "    element = element[1:]\n",
    "    element = element[:-1]\n",
    "    x = element.split()\n",
    "    amenities_list.append(len(x))\n",
    "\n",
    "data.amenities = amenities_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>amenities</th>\n",
       "      <th>flexible</th>\n",
       "      <th>moderate</th>\n",
       "      <th>strict</th>\n",
       "      <th>strict_14_with_grace_period</th>\n",
       "      <th>super_strict_30</th>\n",
       "      <th>super_strict_60</th>\n",
       "      <th>instant_booking_t</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Hotel room</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  accommodates  bathrooms  bedrooms  beds  review_scores_rating  \\\n",
       "0     90             4        1.0       1.0   2.0                  93.0   \n",
       "1     28             1        1.0       1.0   1.0                  89.0   \n",
       "2    125             2        1.0       1.0   1.0                  99.0   \n",
       "\n",
       "   amenities  flexible  moderate  strict  strict_14_with_grace_period  \\\n",
       "0         14         0         0       0                            1   \n",
       "1         30         0         0       0                            1   \n",
       "2         16         0         1       0                            0   \n",
       "\n",
       "   super_strict_30  super_strict_60  instant_booking_t  Entire home/apt  \\\n",
       "0                0                0                  0                1   \n",
       "1                0                0                  0                0   \n",
       "2                0                0                  0                1   \n",
       "\n",
       "   Hotel room  Private room  Shared room  \n",
       "0           0             0            0  \n",
       "1           0             1            0  \n",
       "2           0             0            0  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>price</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>amenities</th>\n",
       "      <th>flexible</th>\n",
       "      <th>moderate</th>\n",
       "      <th>strict</th>\n",
       "      <th>strict_14_with_grace_period</th>\n",
       "      <th>super_strict_30</th>\n",
       "      <th>super_strict_60</th>\n",
       "      <th>instant_booking_t</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <th>Hotel room</th>\n",
       "      <th>Private room</th>\n",
       "      <th>Shared room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "      <td>18184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>69.710460</td>\n",
       "      <td>2.777387</td>\n",
       "      <td>1.112626</td>\n",
       "      <td>1.258194</td>\n",
       "      <td>1.687637</td>\n",
       "      <td>94.729542</td>\n",
       "      <td>15.418390</td>\n",
       "      <td>0.324131</td>\n",
       "      <td>0.342169</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.331775</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.348988</td>\n",
       "      <td>0.493786</td>\n",
       "      <td>0.008799</td>\n",
       "      <td>0.487352</td>\n",
       "      <td>0.010064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>159.992242</td>\n",
       "      <td>1.634657</td>\n",
       "      <td>0.364050</td>\n",
       "      <td>0.605969</td>\n",
       "      <td>1.250107</td>\n",
       "      <td>7.653009</td>\n",
       "      <td>10.852272</td>\n",
       "      <td>0.468062</td>\n",
       "      <td>0.474449</td>\n",
       "      <td>0.010487</td>\n",
       "      <td>0.470864</td>\n",
       "      <td>0.027737</td>\n",
       "      <td>0.032309</td>\n",
       "      <td>0.476663</td>\n",
       "      <td>0.499975</td>\n",
       "      <td>0.093392</td>\n",
       "      <td>0.499854</td>\n",
       "      <td>0.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price  accommodates     bathrooms      bedrooms          beds  \\\n",
       "count  18184.000000  18184.000000  18184.000000  18184.000000  18184.000000   \n",
       "mean      69.710460      2.777387      1.112626      1.258194      1.687637   \n",
       "std      159.992242      1.634657      0.364050      0.605969      1.250107   \n",
       "min        1.000000      1.000000      0.500000      1.000000      1.000000   \n",
       "25%       35.000000      2.000000      1.000000      1.000000      1.000000   \n",
       "50%       50.000000      2.000000      1.000000      1.000000      1.000000   \n",
       "75%       80.000000      4.000000      1.000000      1.000000      2.000000   \n",
       "max     9000.000000     16.000000      8.500000     12.000000     22.000000   \n",
       "\n",
       "       review_scores_rating     amenities      flexible      moderate  \\\n",
       "count          18184.000000  18184.000000  18184.000000  18184.000000   \n",
       "mean              94.729542     15.418390      0.324131      0.342169   \n",
       "std                7.653009     10.852272      0.468062      0.474449   \n",
       "min               20.000000      0.000000      0.000000      0.000000   \n",
       "25%               93.000000      7.000000      0.000000      0.000000   \n",
       "50%               97.000000     13.000000      0.000000      0.000000   \n",
       "75%              100.000000     22.000000      1.000000      1.000000   \n",
       "max              100.000000    105.000000      1.000000      1.000000   \n",
       "\n",
       "             strict  strict_14_with_grace_period  super_strict_30  \\\n",
       "count  18184.000000                 18184.000000     18184.000000   \n",
       "mean       0.000110                     0.331775         0.000770   \n",
       "std        0.010487                     0.470864         0.027737   \n",
       "min        0.000000                     0.000000         0.000000   \n",
       "25%        0.000000                     0.000000         0.000000   \n",
       "50%        0.000000                     0.000000         0.000000   \n",
       "75%        0.000000                     1.000000         0.000000   \n",
       "max        1.000000                     1.000000         1.000000   \n",
       "\n",
       "       super_strict_60  instant_booking_t  Entire home/apt    Hotel room  \\\n",
       "count     18184.000000       18184.000000     18184.000000  18184.000000   \n",
       "mean          0.001045           0.348988         0.493786      0.008799   \n",
       "std           0.032309           0.476663         0.499975      0.093392   \n",
       "min           0.000000           0.000000         0.000000      0.000000   \n",
       "25%           0.000000           0.000000         0.000000      0.000000   \n",
       "50%           0.000000           0.000000         0.000000      0.000000   \n",
       "75%           0.000000           1.000000         1.000000      0.000000   \n",
       "max           1.000000           1.000000         1.000000      1.000000   \n",
       "\n",
       "       Private room   Shared room  \n",
       "count  18184.000000  18184.000000  \n",
       "mean       0.487352      0.010064  \n",
       "std        0.499854      0.099815  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        1.000000      0.000000  \n",
       "max        1.000000      1.000000  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics\n",
    "#from sklearn.grid_search import GridSearchCV\n",
    "#from sklearn.grid_search import RandomizedSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn import datasets\n",
    "from sklearn import model_selection \n",
    "#https://stackoverflow.com/questions/30667525/importerror-no-module-named-sklearn-cross-validation\n",
    "from sklearn import linear_model\n",
    "from sklearn import ensemble\n",
    "\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error \n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split, KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the training and test sets with a 60% and 40% size of original\n",
    "split_data = data.drop(['price'], axis = 1)\n",
    "\n",
    "#train1, test1, train2, test2 = model_selection.train_test_split(split_data,data.price, test_size=0.1,\n",
    " #                                                               train_size = 0.9, random_state=0)\n",
    "\n",
    "#https://stackoverflow.com/questions/24147278/how-do-i-create-test-and-train-samples-from-one-dataframe-with-pandas\n",
    "x_train,x_test,y_train,y_test = model_selection.train_test_split(split_data,data.price, test_size=0.1,\n",
    "                                                                train_size = 0.9, random_state=0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 69.71045974483062\n",
      "standard deviation: 159.98784231378616\n"
     ]
    }
   ],
   "source": [
    "# mean of prices\n",
    "mean = np.mean(data.price)\n",
    "\n",
    "# standard deviation to compare \n",
    "std = np.std(data.price)\n",
    "\n",
    "print(\"mean: \" + str(mean))\n",
    "print (\"standard deviation: \" + str(std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression: median absolute error 15.147959473573316\n",
      "Linear Regression: mean absolute error 28.718172190639223\n",
      "Linear Regression: RMSE 129.37655180216433\n",
      "Linear Regression: R2 score 0.06638804208797744\n",
      "Model accuracy on train set: 0.12\n",
      "Model accuracy on test set: 0.07\n"
     ]
    }
   ],
   "source": [
    "# linear regression testing\n",
    "linear_reg = linear_model.LinearRegression()\n",
    "linear_reg.fit(x_train, y_train)\n",
    "LR_medianAbsoluteError = metrics.median_absolute_error(test2, linear_reg.predict(test1))\n",
    "print(\"Linear Regression: median absolute error \" + str(LR_medianAbsoluteError))\n",
    "LR_meanAbsoluteError = metrics.mean_absolute_error(test2, linear_reg.predict(test1))\n",
    "print(\"Linear Regression: mean absolute error \" + str(LR_meanAbsoluteError))\n",
    "LR_RMSE = np.sqrt(metrics.mean_squared_error(test2, linear_reg.predict(test1)))\n",
    "print(\"Linear Regression: RMSE \" + str(LR_RMSE))\n",
    "LR_R2score = metrics.r2_score(test2, linear_reg.predict(test1))\n",
    "print (\"Linear Regression: R2 score \" + str(LR_R2score))\n",
    "print('Model accuracy on train set: {:.2f}'.format(linear_reg.score(x_train, y_train)))\n",
    "print('Model accuracy on test set: {:.2f}'.format(linear_reg.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression: median absolute error 16.086011814306346\n",
      "Linear Regression: mean absolute error 28.320278681598424\n",
      "Linear Regression: RMSE 126.7935892720654\n",
      "Linear Regression: R2 score 0.10329445830398853\n",
      "Model accuracy on train set: 0.09\n",
      "Model accuracy on test set: 0.10\n"
     ]
    }
   ],
   "source": [
    "# linear regression testing\n",
    "linear_reg = linear_model.LinearRegression()\n",
    "linear_reg.fit(x_test, y_test)\n",
    "LR_medianAbsoluteError = metrics.median_absolute_error(test2, linear_reg.predict(test1))\n",
    "print(\"Linear Regression: median absolute error \" + str(LR_medianAbsoluteError))\n",
    "LR_meanAbsoluteError = metrics.mean_absolute_error(test2, linear_reg.predict(test1))\n",
    "print(\"Linear Regression: mean absolute error \" + str(LR_meanAbsoluteError))\n",
    "LR_RMSE = np.sqrt(metrics.mean_squared_error(test2, linear_reg.predict(test1)))\n",
    "print(\"Linear Regression: RMSE \" + str(LR_RMSE))\n",
    "LR_R2score = metrics.r2_score(test2, linear_reg.predict(test1))\n",
    "print (\"Linear Regression: R2 score \" + str(LR_R2score))\n",
    "print('Model accuracy on train set: {:.2f}'.format(linear_reg.score(x_train, y_train)))\n",
    "print('Model accuracy on test set: {:.2f}'.format(linear_reg.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge: median absolute error 15.140536185040801\n",
      "Ridge: mean absolute error 28.773163320334728\n",
      "Ridge: RMSE 129.36975226635977\n",
      "Ridge: R2 score 0.06648617364319864\n",
      "Model accuracy on train set: 0.12\n",
      "Model accuracy on test set: 0.07\n"
     ]
    }
   ],
   "source": [
    "# ridge model testing\n",
    "ridge = linear_model.Ridge()\n",
    "ridge.fit(x_train, y_train)\n",
    "ridge_error = metrics.median_absolute_error(test2, ridge.predict(test1))\n",
    "print (\"Ridge: median absolute error \" + str(ridge_error))\n",
    "ridge_error = metrics.mean_absolute_error(test2, ridge.predict(test1))\n",
    "print (\"Ridge: mean absolute error \" + str(ridge_error))\n",
    "Ridge_RMSE = np.sqrt(metrics.mean_squared_error(test2, ridge.predict(test1)))\n",
    "print (\"Ridge: RMSE \" + str(Ridge_RMSE))\n",
    "Ridge_R2score = metrics.r2_score(test2, ridge.predict(test1))\n",
    "print (\"Ridge: R2 score \" + str(Ridge_R2score))\n",
    "print('Model accuracy on train set: {:.2f}'.format(ridge.score(x_train, y_train)))\n",
    "print('Model accuracy on test set: {:.2f}'.format(ridge.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge: median absolute error 16.170663517939772\n",
      "Ridge: mean absolute error 28.325268068627206\n",
      "Ridge: RMSE 126.81247074250683\n",
      "Ridge: R2 score 0.10302737256366967\n"
     ]
    }
   ],
   "source": [
    "# ridge model testing\n",
    "ridge = linear_model.Ridge()\n",
    "ridge.fit(x_test, y_test)\n",
    "ridge_error = metrics.median_absolute_error(test2, ridge.predict(test1))\n",
    "print (\"Ridge: median absolute error \" + str(ridge_error))\n",
    "ridge_error = metrics.mean_absolute_error(test2, ridge.predict(test1))\n",
    "print (\"Ridge: mean absolute error \" + str(ridge_error))\n",
    "Ridge_RMSE = np.sqrt(metrics.mean_squared_error(test2, ridge.predict(test1)))\n",
    "print (\"Ridge: RMSE \" + str(Ridge_RMSE))\n",
    "Ridge_R2score = metrics.r2_score(test2, ridge.predict(test1))\n",
    "print (\"Ridge: R2 score \" + str(Ridge_R2score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree: median absolute error  17.0\n",
      "Decision tree: mean absolute error 38.10314660586676\n",
      "Decision tree: RMSE 250.37767155243955\n",
      "Decision tree: R2 score -2.49660403394318\n",
      "Model accuracy on train set: 0.99\n",
      "Model accuracy on test set: -2.50\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/39158003/confused-about-random-state-in-decision-tree-of-scikit-learn\n",
    "decisionTree = DecisionTreeRegressor(random_state=0)\n",
    "decisionTree.fit(x_train, y_train)\n",
    "decisionTree_medianAbsoluteError = metrics.median_absolute_error(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: median absolute error  \" + str(decisionTree_medianAbsoluteError))\n",
    "decisionTree_meanAbsoluteError = metrics.mean_absolute_error(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: mean absolute error \" + str(decisionTree_meanAbsoluteError))\n",
    "DT_RMSE = np.sqrt(metrics.mean_squared_error(test2, decisionTree.predict(test1)))\n",
    "print (\"Decision tree: RMSE \" + str(DT_RMSE))\n",
    "DT_R2score = metrics.r2_score(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: R2 score \" + str(DT_R2score))\n",
    "print('Model accuracy on train set: {:.2f}'.format(decisionTree.score(x_train, y_train)))\n",
    "print('Model accuracy on test set: {:.2f}'.format(decisionTree.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree: median absolute error  0.0\n",
      "Decision tree: mean absolute error 5.870271473075211\n",
      "Decision tree: RMSE 104.19143305779377\n",
      "Decision tree: R2 score 0.39449283787030587\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/39158003/confused-about-random-state-in-decision-tree-of-scikit-learn\n",
    "decisionTree = DecisionTreeRegressor(random_state=0)\n",
    "decisionTree.fit(x_test, y_test)\n",
    "decisionTree_medianAbsoluteError = metrics.median_absolute_error(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: median absolute error  \" + str(decisionTree_medianAbsoluteError))\n",
    "decisionTree_meanAbsoluteError = metrics.mean_absolute_error(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: mean absolute error \" + str(decisionTree_meanAbsoluteError))\n",
    "DT_RMSE = np.sqrt(metrics.mean_squared_error(test2, decisionTree.predict(test1)))\n",
    "print (\"Decision tree: RMSE \" + str(DT_RMSE))\n",
    "DT_R2score = metrics.r2_score(test2, decisionTree.predict(test1))\n",
    "print (\"Decision tree: R2 score \" + str(DT_R2score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: median absolute error  15.334166666666675\n",
      "Random forest: mean absolute error 31.058277473214492\n",
      "Random forest: RMSE 161.40526490178266\n",
      "Random forest: R2 score -0.45308378185452014\n",
      "Model accuracy on train set: 0.89\n",
      "Model accuracy on test set: -0.45\n"
     ]
    }
   ],
   "source": [
    "RF_regr = RandomForestRegressor(n_estimators = 50, random_state = 0) \n",
    "RF_regr.fit(x_train, y_train)\n",
    "randomForest_medianAbsoluteError = metrics.median_absolute_error(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: median absolute error  \" + str(randomForest_medianAbsoluteError))\n",
    "randomForest_meanAbsoluteError = metrics.mean_absolute_error(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: mean absolute error \" + str(randomForest_meanAbsoluteError))\n",
    "RF_RMSE = np.sqrt(metrics.mean_squared_error(test2, RF_regr.predict(test1)))\n",
    "print (\"Random forest: RMSE \" + str(RF_RMSE))\n",
    "RF_R2score = metrics.r2_score(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: R2 score \" + str(RF_R2score))\n",
    "print('Model accuracy on train set: {:.2f}'.format(RF_regr.score(x_train, y_train)))\n",
    "print('Model accuracy on test set: {:.2f}'.format(RF_regr.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: median absolute error  5.789999999999999\n",
      "Random forest: mean absolute error 15.536189702332365\n",
      "Random forest: RMSE 107.15625397659004\n",
      "Random forest: R2 score 0.35954251217257094\n"
     ]
    }
   ],
   "source": [
    "RF_regr = RandomForestRegressor(n_estimators = 50, random_state = 0) \n",
    "RF_regr.fit(x_test, y_test)\n",
    "randomForest_medianAbsoluteError = metrics.median_absolute_error(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: median absolute error  \" + str(randomForest_medianAbsoluteError))\n",
    "randomForest_meanAbsoluteError = metrics.mean_absolute_error(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: mean absolute error \" + str(randomForest_meanAbsoluteError))\n",
    "RF_RMSE = np.sqrt(metrics.mean_squared_error(test2, RF_regr.predict(test1)))\n",
    "print (\"Random forest: RMSE \" + str(RF_RMSE))\n",
    "RF_R2score = metrics.r2_score(test2, RF_regr.predict(test1))\n",
    "print (\"Random forest: R2 score \" + str(RF_R2score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting for value of k = 2\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 3\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 4\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 5\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 6\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 7\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 8\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 9\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 10\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 11\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 12\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 13\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 14\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 15\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 16\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 17\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 18\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 19\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n",
      "Starting for value of k = 20\n",
      "KNN: median absolute error  15.0\n",
      "KNN: mean absolute error  31.34194612424409\n",
      "KNN: RMSE 130.84477288158783\n",
      "KNN: R2 score 0.04507774030066469\n"
     ]
    }
   ],
   "source": [
    "for k in range(2, 21):\n",
    "    print(\"Starting for value of k = \" + str(k))\n",
    "    knn = KNeighborsClassifier(n_neighbors = 2)\n",
    "    knn.fit(x_train, y_train)\n",
    "    knn_medianAbsoluteError = metrics.median_absolute_error(test2, knn.predict(test1))\n",
    "    print (\"KNN: median absolute error  \" + str(knn_medianAbsoluteError))\n",
    "    knn_meanAbsoluteError = metrics.mean_absolute_error(test2, knn.predict(test1))\n",
    "    print (\"KNN: mean absolute error  \" + str(knn_meanAbsoluteError))\n",
    "    knn_RMSE = np.sqrt(metrics.mean_squared_error(test2, knn.predict(test1)))\n",
    "    print (\"KNN: RMSE \" + str(knn_RMSE))\n",
    "    knn_R2score = metrics.r2_score(test2, knn.predict(test1))\n",
    "    print (\"KNN: R2 score \" + str(knn_R2score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting for value of k = 2\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 3\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 4\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 5\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 6\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 7\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 8\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 9\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 10\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 11\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 12\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 13\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 14\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 15\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 16\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 17\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 18\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 19\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n",
      "Starting for value of k = 20\n",
      "KNN: median absolute error  1.0\n",
      "KNN: mean absolute error  21.416712479384277\n",
      "KNN: RMSE 129.81844005255974\n",
      "KNN: R2 score 0.05999960942346316\n"
     ]
    }
   ],
   "source": [
    "for k in range(2, 21):\n",
    "    print(\"Starting for value of k = \" + str(k))\n",
    "    knn = KNeighborsClassifier(n_neighbors = 2)\n",
    "    knn.fit(x_test, y_test)\n",
    "    knn_medianAbsoluteError = metrics.median_absolute_error(test2, knn.predict(test1))\n",
    "    print (\"KNN: median absolute error  \" + str(knn_medianAbsoluteError))\n",
    "    knn_meanAbsoluteError = metrics.mean_absolute_error(test2, knn.predict(test1))\n",
    "    print (\"KNN: mean absolute error  \" + str(knn_meanAbsoluteError))\n",
    "    knn_RMSE = np.sqrt(metrics.mean_squared_error(test2, knn.predict(test1)))\n",
    "    print (\"KNN: RMSE \" + str(knn_RMSE))\n",
    "    knn_R2score = metrics.r2_score(test2, knn.predict(test1))\n",
    "    print (\"KNN: R2 score \" + str(knn_R2score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the errors that came for all the algorithms. Let's enhance our learnings and implement\n",
    "\n",
    "1. Bagging regressor\n",
    "2. Gradient boost regressor\n",
    "3. ada boost regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score, make_scorer\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanAbsoluteError = make_scorer(mean_absolute_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $36.84220397805849\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=meanAbsoluteError)\n",
    "preds = baggingRegressor.fit(x_train, y_train)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $36.286586521041066\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=meanAbsoluteError)\n",
    "preds = baggingRegressor.fit(x_test, y_test)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanSquaredError = make_scorer(mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $25832.6588132069\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=meanSquaredError)\n",
    "preds = baggingRegressor.fit(x_train, y_train)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $17576.75249964431\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=meanSquaredError)\n",
    "preds = baggingRegressor.fit(x_test, y_test)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2Score = make_scorer(r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $0.31853861338004663\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=r2Score)\n",
    "preds = baggingRegressor.fit(x_train, y_train)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaggingRegressor: $0.2264498496593887\n"
     ]
    }
   ],
   "source": [
    "# 1. Bagging regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_features\", \"bootstrap\", \"oob_score\"]\n",
    "parameterValues = [[100,500,1000], [1,5,10], [True, False], [False]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "baggingRegressor = GridSearchCV(ensemble.BaggingRegressor(), cv = 3, param_grid = parametersFinal,\n",
    "                                scoring=r2Score)\n",
    "preds = baggingRegressor.fit(x_test, y_test)\n",
    "br_best_estimator = baggingRegressor.best_estimator_\n",
    "print(\"BaggingRegressor: \" + \"$\" + str(abs(baggingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $37.41615556508386\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=meanAbsoluteError)\n",
    "preds = gradientBoostingRegressor.fit(x_train, y_train)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $35.70564203839602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=meanAbsoluteError)\n",
    "preds = gradientBoostingRegressor.fit(x_test, y_test)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $26184.48937058045\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=meanSquaredError)\n",
    "preds = gradientBoostingRegressor.fit(x_train, y_train)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $17762.35745995721\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=meanSquaredError)\n",
    "preds = gradientBoostingRegressor.fit(x_test, y_test)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $0.37345968751777725\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=r2Score)\n",
    "preds = gradientBoostingRegressor.fit(x_train, y_train)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Raghvendra\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor: $0.2968270452341068\n"
     ]
    }
   ],
   "source": [
    "# 2. Gradient boosting regressor\n",
    "\n",
    "parameterNames = [\"n_estimators\", \"max_depth\", \"learning_rate\", \"loss\", \"min_samples_split\"]\n",
    "parameterValues = [[100,300,500], [1,2,3,4], [0.01, 0.02], ['ls', 'lad'], [2,3]]\n",
    "\n",
    "parametersFinal = dict(zip(parameterNames, parameterValues))\n",
    "gradientBoostingRegressor = GridSearchCV(ensemble.GradientBoostingRegressor(), cv = 3, param_grid = parametersFinal, \n",
    "                   scoring=r2Score)\n",
    "preds = gradientBoostingRegressor.fit(x_test, y_test)\n",
    "gbr_best_estimator = gradientBoostingRegressor.best_estimator_\n",
    "print(\"GradientBoostingRegressor: \" + \"$\" + str(abs(gradientBoostingRegressor.best_score_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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