{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I have used RandomForestRegressor to get RMSE of 0.77 which can be further improved to a better value.I have dropped some attributes with null values and used One hot encoding for categorical data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dv/qnpc2tm905n3c2nrmq03c65w0000gn/T/ipykernel_56148/554524768.py:1: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  airbnb = pd.read_csv(\"./airbnb/AB_US_2020.csv\")\n"
     ]
    }
   ],
   "source": [
    "airbnb = pd.read_csv(\"./airbnb/AB_US_2020.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38585</td>\n",
       "      <td>Charming Victorian home - twin beds + breakfast</td>\n",
       "      <td>165529</td>\n",
       "      <td>Evelyne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28804</td>\n",
       "      <td>35.65146</td>\n",
       "      <td>-82.62792</td>\n",
       "      <td>Private room</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>16/02/20</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Asheville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80905</td>\n",
       "      <td>French Chic Loft</td>\n",
       "      <td>427027</td>\n",
       "      <td>Celeste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28801</td>\n",
       "      <td>35.59779</td>\n",
       "      <td>-82.55540</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>07/09/20</td>\n",
       "      <td>1.03</td>\n",
       "      <td>11</td>\n",
       "      <td>288</td>\n",
       "      <td>Asheville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108061</td>\n",
       "      <td>Walk to stores/parks/downtown. Fenced yard/Pet...</td>\n",
       "      <td>320564</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28801</td>\n",
       "      <td>35.60670</td>\n",
       "      <td>-82.55563</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>89</td>\n",
       "      <td>30/11/19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>2</td>\n",
       "      <td>298</td>\n",
       "      <td>Asheville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155305</td>\n",
       "      <td>Cottage! BonPaul + Sharky's Hostel</td>\n",
       "      <td>746673</td>\n",
       "      <td>BonPaul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28806</td>\n",
       "      <td>35.57864</td>\n",
       "      <td>-82.59578</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>267</td>\n",
       "      <td>22/09/20</td>\n",
       "      <td>2.39</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Asheville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160594</td>\n",
       "      <td>Historic Grove Park</td>\n",
       "      <td>769252</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28801</td>\n",
       "      <td>35.61442</td>\n",
       "      <td>-82.54127</td>\n",
       "      <td>Private room</td>\n",
       "      <td>125</td>\n",
       "      <td>30</td>\n",
       "      <td>58</td>\n",
       "      <td>19/10/15</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Asheville</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                               name  host_id  \\\n",
       "0   38585    Charming Victorian home - twin beds + breakfast   165529   \n",
       "1   80905                                   French Chic Loft   427027   \n",
       "2  108061  Walk to stores/parks/downtown. Fenced yard/Pet...   320564   \n",
       "3  155305                 Cottage! BonPaul + Sharky's Hostel   746673   \n",
       "4  160594                                Historic Grove Park   769252   \n",
       "\n",
       "   host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0    Evelyne                 NaN         28804  35.65146  -82.62792   \n",
       "1    Celeste                 NaN         28801  35.59779  -82.55540   \n",
       "2       Lisa                 NaN         28801  35.60670  -82.55563   \n",
       "3    BonPaul                 NaN         28806  35.57864  -82.59578   \n",
       "4  Elizabeth                 NaN         28801  35.61442  -82.54127   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room     60               1                138    16/02/20   \n",
       "1  Entire home/apt    470               1                114    07/09/20   \n",
       "2  Entire home/apt     75              30                 89    30/11/19   \n",
       "3  Entire home/apt     90               1                267    22/09/20   \n",
       "4     Private room    125              30                 58    19/10/15   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
       "0               1.14                               1                 0   \n",
       "1               1.03                              11               288   \n",
       "2               0.81                               2               298   \n",
       "3               2.39                               5                 0   \n",
       "4               0.52                               1                 0   \n",
       "\n",
       "        city  \n",
       "0  Asheville  \n",
       "1  Asheville  \n",
       "2  Asheville  \n",
       "3  Asheville  \n",
       "4  Asheville  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 226030 entries, 0 to 226029\n",
      "Data columns (total 17 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   id                              226030 non-null  int64  \n",
      " 1   name                            226002 non-null  object \n",
      " 2   host_id                         226030 non-null  int64  \n",
      " 3   host_name                       225997 non-null  object \n",
      " 4   neighbourhood_group             110185 non-null  object \n",
      " 5   neighbourhood                   226030 non-null  object \n",
      " 6   latitude                        226030 non-null  float64\n",
      " 7   longitude                       226030 non-null  float64\n",
      " 8   room_type                       226030 non-null  object \n",
      " 9   price                           226030 non-null  int64  \n",
      " 10  minimum_nights                  226030 non-null  int64  \n",
      " 11  number_of_reviews               226030 non-null  int64  \n",
      " 12  last_review                     177428 non-null  object \n",
      " 13  reviews_per_month               177428 non-null  float64\n",
      " 14  calculated_host_listings_count  226030 non-null  int64  \n",
      " 15  availability_365                226030 non-null  int64  \n",
      " 16  city                            226030 non-null  object \n",
      "dtypes: float64(3), int64(7), object(7)\n",
      "memory usage: 29.3+ MB\n"
     ]
    }
   ],
   "source": [
    "airbnb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "name                                  28\n",
       "host_id                                0\n",
       "host_name                             33\n",
       "neighbourhood_group               115845\n",
       "neighbourhood                          0\n",
       "latitude                               0\n",
       "longitude                              0\n",
       "room_type                              0\n",
       "price                                  0\n",
       "minimum_nights                         0\n",
       "number_of_reviews                      0\n",
       "last_review                        48602\n",
       "reviews_per_month                  48602\n",
       "calculated_host_listings_count         0\n",
       "availability_365                       0\n",
       "city                                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='room_type', ylabel='price'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(x='room_type',y='price',data=airbnb,palette='spring')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that price depends on the type of room and shared room are of lowest price and hotel room are of highest price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x139442340>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='number_of_reviews',y='price',data=airbnb, palette='spring')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "airbnb['neighbourhood_group'].fillna('Others',inplace=True)\n",
    "airbnb.drop(['name','host_name'],axis=1,inplace=True)\n",
    "airbnb['last_review'] = pd.to_datetime(airbnb['last_review'],infer_datetime_format=True)\n",
    "airbnb['reviews_per_month'].fillna(airbnb['reviews_per_month'].mean(),inplace=True)\n",
    "airbnb[\"last_review\"] = airbnb[\"last_review\"].replace(np.nan, airbnb[\"last_review\"].mode().iloc[0])\n",
    "airbnb.drop(['id','host_id'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we dropped 'name' and 'host_name' as price don't depend on them. And change the format of 'last_review'. And replaced the missing values of 'reviews_per_month' with it's mean value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "airbnb['neighbourhood_group'] = label_encoder.fit_transform(airbnb['neighbourhood_group'])\n",
    "airbnb['neighbourhood'] = label_encoder.fit_transform(airbnb['neighbourhood'])\n",
    "airbnb['room_type'] = label_encoder.fit_transform(airbnb['room_type'])\n",
    "airbnb['city'] = label_encoder.fit_transform(airbnb['city'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    2\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb['room_type'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I have used LabelEncoder for categorical variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=12,12\n",
    "g = sns.heatmap(airbnb.corr(),annot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime \n",
    "airbnb['last_review'] = airbnb['last_review'].map(datetime.datetime.toordinal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "z_scores = stats.zscore(airbnb)\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
    "new_airbnb = airbnb[filtered_entries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "#scaler.fit(new_df)\n",
    "scaled_features = scaler.fit_transform(new_airbnb)\n",
    "scaled_features_airbnb = pd.DataFrame(scaled_features, index=new_airbnb.index, columns=new_airbnb.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.621334</td>\n",
       "      <td>-1.767512</td>\n",
       "      <td>-0.004938</td>\n",
       "      <td>0.775638</td>\n",
       "      <td>1.381058</td>\n",
       "      <td>-0.599407</td>\n",
       "      <td>-0.362783</td>\n",
       "      <td>2.555047</td>\n",
       "      <td>0.143892</td>\n",
       "      <td>-0.148009</td>\n",
       "      <td>-0.395247</td>\n",
       "      <td>-1.143297</td>\n",
       "      <td>-1.88295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.621334</td>\n",
       "      <td>-1.772330</td>\n",
       "      <td>-0.012851</td>\n",
       "      <td>0.778424</td>\n",
       "      <td>-0.686353</td>\n",
       "      <td>1.386301</td>\n",
       "      <td>-0.362783</td>\n",
       "      <td>1.996818</td>\n",
       "      <td>0.701810</td>\n",
       "      <td>-0.245114</td>\n",
       "      <td>0.022237</td>\n",
       "      <td>0.921239</td>\n",
       "      <td>-1.88295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.621334</td>\n",
       "      <td>-1.772330</td>\n",
       "      <td>-0.011537</td>\n",
       "      <td>0.778415</td>\n",
       "      <td>-0.686353</td>\n",
       "      <td>-0.526759</td>\n",
       "      <td>0.812551</td>\n",
       "      <td>1.415331</td>\n",
       "      <td>-0.158313</td>\n",
       "      <td>-0.439324</td>\n",
       "      <td>-0.353498</td>\n",
       "      <td>0.992924</td>\n",
       "      <td>-1.88295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.621334</td>\n",
       "      <td>-1.767512</td>\n",
       "      <td>-0.009789</td>\n",
       "      <td>0.778525</td>\n",
       "      <td>-0.686353</td>\n",
       "      <td>-0.241010</td>\n",
       "      <td>-0.119610</td>\n",
       "      <td>0.601248</td>\n",
       "      <td>0.016036</td>\n",
       "      <td>-0.721810</td>\n",
       "      <td>-0.395247</td>\n",
       "      <td>0.964250</td>\n",
       "      <td>-1.88295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.621334</td>\n",
       "      <td>-1.762694</td>\n",
       "      <td>-0.014965</td>\n",
       "      <td>0.776821</td>\n",
       "      <td>1.381058</td>\n",
       "      <td>-0.657525</td>\n",
       "      <td>-0.362783</td>\n",
       "      <td>2.531787</td>\n",
       "      <td>-0.158313</td>\n",
       "      <td>0.037373</td>\n",
       "      <td>-0.395247</td>\n",
       "      <td>-1.143297</td>\n",
       "      <td>-1.88295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood_group  neighbourhood  latitude  longitude  room_type  \\\n",
       "0             0.621334      -1.767512 -0.004938   0.775638   1.381058   \n",
       "1             0.621334      -1.772330 -0.012851   0.778424  -0.686353   \n",
       "2             0.621334      -1.772330 -0.011537   0.778415  -0.686353   \n",
       "5             0.621334      -1.767512 -0.009789   0.778525  -0.686353   \n",
       "6             0.621334      -1.762694 -0.014965   0.776821   1.381058   \n",
       "\n",
       "      price  minimum_nights  number_of_reviews  last_review  \\\n",
       "0 -0.599407       -0.362783           2.555047     0.143892   \n",
       "1  1.386301       -0.362783           1.996818     0.701810   \n",
       "2 -0.526759        0.812551           1.415331    -0.158313   \n",
       "5 -0.241010       -0.119610           0.601248     0.016036   \n",
       "6 -0.657525       -0.362783           2.531787    -0.158313   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
       "0          -0.148009                       -0.395247         -1.143297   \n",
       "1          -0.245114                        0.022237          0.921239   \n",
       "2          -0.439324                       -0.353498          0.992924   \n",
       "5          -0.721810                       -0.395247          0.964250   \n",
       "6           0.037373                       -0.395247         -1.143297   \n",
       "\n",
       "      city  \n",
       "0 -1.88295  \n",
       "1 -1.88295  \n",
       "2 -1.88295  \n",
       "5 -1.88295  \n",
       "6 -1.88295  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features_airbnb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SPLITTING OF DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = scaled_features_airbnb.drop('price',axis=1)\n",
    "y = scaled_features_airbnb['price']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y , test_size = 0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RANDOM FOREST REGRESSOR**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 200, random_state = 0)\n",
    "model=regressor.fit(X_train, y_train)  \n",
    "y1 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.6030891447547098\n",
      "RMSE 0.7765881435836565\n",
      "Adj R^2 value: 0.39539775746287953\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "import math\n",
    "print('MSE',mean_squared_error(y_test, y1))\n",
    "print('RMSE',math.sqrt(mean_squared_error(y_test, y1)))\n",
    "print('Adj R^2 value:',1 - (1-regressor.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))"
   ]
  }
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