hotel_booking_cancellation_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "W_uzh6rg9eJd",
    "outputId": "a78da305-dfce-4889-d880-aaa71bf4f68a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/gdrive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "cKAV2rhqDrCc",
    "outputId": "d9114de0-969e-45ab-8832-b895a04eb748"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from zipfile import ZipFile\n",
    "file_name = \"/content/gdrive/MyDrive/Colab Notebooks/hbc/hotel.zip\"\n",
    "\n",
    "with ZipFile(file_name,'r') as zip:\n",
    "  zip.extractall()\n",
    "  print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iXtYRwfhkP5s"
   },
   "source": [
    "**Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "kThSuDntDuhz"
   },
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from IPython.display import display_html\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "d7K37-n3ELB7"
   },
   "outputs": [],
   "source": [
    "h1 = pd.read_csv('/content/H1.csv')\n",
    "h2 = pd.read_csv('/content/H2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ffme6FhzENSo"
   },
   "outputs": [],
   "source": [
    "h1.loc[:, 'id'] = range(1, len(h1) + 1)\n",
    "\n",
    "start = h1['id'].max() + 1\n",
    "stop = start + len(h2)\n",
    "h2.loc[:, 'id'] = range(start, stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "HUJ5h_1MEPsk"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([h1, h2], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "hOdIhzuMETX7",
    "outputId": "d8c24f44-acbb-4e86-8a07-88d300db1737"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IsCanceled', 'LeadTime', 'ArrivalDateYear'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "MToI4nEjEW_I"
   },
   "outputs": [],
   "source": [
    "#converting it to snake_case\n",
    "def convert(name):\n",
    "    s1 = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', name)\n",
    "    return re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', s1).lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "kPxssfVBEixg",
    "outputId": "fbb40bc3-3fea-4fed-ffcf-e6bde409cc83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['IsCanceled', 'LeadTime', 'ArrivalDateYear', 'ArrivalDateMonth',\n",
      "       'ArrivalDateWeekNumber', 'ArrivalDateDayOfMonth',\n",
      "       'StaysInWeekendNights', 'StaysInWeekNights', 'Adults', 'Children',\n",
      "       'Babies', 'Meal', 'Country', 'MarketSegment', 'DistributionChannel',\n",
      "       'IsRepeatedGuest', 'PreviousCancellations',\n",
      "       'PreviousBookingsNotCanceled', 'ReservedRoomType', 'AssignedRoomType',\n",
      "       'BookingChanges', 'DepositType', 'Agent', 'Company',\n",
      "       'DaysInWaitingList', 'CustomerType', 'ADR', 'RequiredCarParkingSpaces',\n",
      "       'TotalOfSpecialRequests', 'ReservationStatus', 'ReservationStatusDate',\n",
      "       'id'],\n",
      "      dtype='object')\n",
      "['is_canceled', 'lead_time', 'arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month', 'stays_in_weekend_nights', 'stays_in_week_nights', 'adults', 'children', 'babies', 'meal', 'country', 'market_segment', 'distribution_channel', 'is_repeated_guest', 'previous_cancellations', 'previous_bookings_not_canceled', 'reserved_room_type', 'assigned_room_type', 'booking_changes', 'deposit_type', 'agent', 'company', 'days_in_waiting_list', 'customer_type', 'adr', 'required_car_parking_spaces', 'total_of_special_requests', 'reservation_status', 'reservation_status_date', 'id']\n"
     ]
    }
   ],
   "source": [
    "columns = [convert(col) for col in df.columns]\n",
    "print(df.columns)\n",
    "df.columns = columns\n",
    "print(columns);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "kHXVxZjREkuy",
    "outputId": "62477dab-ba85-4dd4-845b-f4bf0f63bdd1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "# rows and cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "7c_VOQvIEr-5",
    "outputId": "bffe67a6-9a0e-49fa-e8b7-ed9d33a87258"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_canceled                                      0\n",
       "lead_time                                      342\n",
       "arrival_date_year                             2015\n",
       "arrival_date_month                            July\n",
       "arrival_date_week_number                        27\n",
       "arrival_date_day_of_month                        1\n",
       "stays_in_weekend_nights                          0\n",
       "stays_in_week_nights                             0\n",
       "adults                                           2\n",
       "children                                       0.0\n",
       "babies                                           0\n",
       "meal                                     BB       \n",
       "country                                        PRT\n",
       "market_segment                              Direct\n",
       "distribution_channel                        Direct\n",
       "is_repeated_guest                                0\n",
       "previous_cancellations                           0\n",
       "previous_bookings_not_canceled                   0\n",
       "reserved_room_type                C               \n",
       "assigned_room_type                C               \n",
       "booking_changes                                  3\n",
       "deposit_type                       No Deposit     \n",
       "agent                                         NULL\n",
       "company                                       NULL\n",
       "days_in_waiting_list                             0\n",
       "customer_type                            Transient\n",
       "adr                                            0.0\n",
       "required_car_parking_spaces                      0\n",
       "total_of_special_requests                        0\n",
       "reservation_status                       Check-Out\n",
       "reservation_status_date                 2015-07-01\n",
       "id                                               1\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#attributes\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 770
    },
    "id": "s5AFAkOAExU8",
    "outputId": "54ba7c0f-951c-4a91-f73a-ae33825b3d51"
   },
   "outputs": [
    {
     "data": {
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>...</th>\n",
       "      <th>agent</th>\n",
       "      <th>company</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
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       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
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       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>304</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
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       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>107.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>303</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>103.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>82.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-05-06</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>105.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>123.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>145.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>97.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>241</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>154.77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>241</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>94.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>97.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Contract</td>\n",
       "      <td>97.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>88.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NULL</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>107.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>250</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>153.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 32 columns</p>\n",
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      "text/plain": [
       "    is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0             0        342               2015               July   \n",
       "1             0        737               2015               July   \n",
       "2             0          7               2015               July   \n",
       "3             0         13               2015               July   \n",
       "4             0         14               2015               July   \n",
       "5             0         14               2015               July   \n",
       "6             0          0               2015               July   \n",
       "7             0          9               2015               July   \n",
       "8             1         85               2015               July   \n",
       "9             1         75               2015               July   \n",
       "10            1         23               2015               July   \n",
       "11            0         35               2015               July   \n",
       "12            0         68               2015               July   \n",
       "13            0         18               2015               July   \n",
       "14            0         37               2015               July   \n",
       "15            0         68               2015               July   \n",
       "16            0         37               2015               July   \n",
       "17            0         12               2015               July   \n",
       "18            0          0               2015               July   \n",
       "19            0          7               2015               July   \n",
       "\n",
       "    arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                         27                          1   \n",
       "1                         27                          1   \n",
       "2                         27                          1   \n",
       "3                         27                          1   \n",
       "4                         27                          1   \n",
       "5                         27                          1   \n",
       "6                         27                          1   \n",
       "7                         27                          1   \n",
       "8                         27                          1   \n",
       "9                         27                          1   \n",
       "10                        27                          1   \n",
       "11                        27                          1   \n",
       "12                        27                          1   \n",
       "13                        27                          1   \n",
       "14                        27                          1   \n",
       "15                        27                          1   \n",
       "16                        27                          1   \n",
       "17                        27                          1   \n",
       "18                        27                          1   \n",
       "19                        27                          1   \n",
       "\n",
       "    stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n",
       "0                         0                     0       2       0.0  ...   \n",
       "1                         0                     0       2       0.0  ...   \n",
       "2                         0                     1       1       0.0  ...   \n",
       "3                         0                     1       1       0.0  ...   \n",
       "4                         0                     2       2       0.0  ...   \n",
       "5                         0                     2       2       0.0  ...   \n",
       "6                         0                     2       2       0.0  ...   \n",
       "7                         0                     2       2       0.0  ...   \n",
       "8                         0                     3       2       0.0  ...   \n",
       "9                         0                     3       2       0.0  ...   \n",
       "10                        0                     4       2       0.0  ...   \n",
       "11                        0                     4       2       0.0  ...   \n",
       "12                        0                     4       2       0.0  ...   \n",
       "13                        0                     4       2       1.0  ...   \n",
       "14                        0                     4       2       0.0  ...   \n",
       "15                        0                     4       2       0.0  ...   \n",
       "16                        0                     4       2       0.0  ...   \n",
       "17                        0                     1       2       0.0  ...   \n",
       "18                        0                     1       2       0.0  ...   \n",
       "19                        0                     4       2       0.0  ...   \n",
       "\n",
       "          agent      company days_in_waiting_list customer_type     adr  \\\n",
       "0          NULL         NULL                    0     Transient    0.00   \n",
       "1          NULL         NULL                    0     Transient    0.00   \n",
       "2          NULL         NULL                    0     Transient   75.00   \n",
       "3           304         NULL                    0     Transient   75.00   \n",
       "4           240         NULL                    0     Transient   98.00   \n",
       "5           240         NULL                    0     Transient   98.00   \n",
       "6          NULL         NULL                    0     Transient  107.00   \n",
       "7           303         NULL                    0     Transient  103.00   \n",
       "8           240         NULL                    0     Transient   82.00   \n",
       "9            15         NULL                    0     Transient  105.50   \n",
       "10          240         NULL                    0     Transient  123.00   \n",
       "11          240         NULL                    0     Transient  145.00   \n",
       "12          240         NULL                    0     Transient   97.00   \n",
       "13          241         NULL                    0     Transient  154.77   \n",
       "14          241         NULL                    0     Transient   94.71   \n",
       "15          240         NULL                    0     Transient   97.00   \n",
       "16            8         NULL                    0      Contract   97.50   \n",
       "17          240         NULL                    0     Transient   88.20   \n",
       "18         NULL          110                    0     Transient  107.42   \n",
       "19          250         NULL                    0     Transient  153.00   \n",
       "\n",
       "    required_car_parking_spaces  total_of_special_requests  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          1   \n",
       "5                             0                          1   \n",
       "6                             0                          0   \n",
       "7                             0                          1   \n",
       "8                             0                          1   \n",
       "9                             0                          0   \n",
       "10                            0                          0   \n",
       "11                            0                          0   \n",
       "12                            0                          3   \n",
       "13                            0                          1   \n",
       "14                            0                          0   \n",
       "15                            0                          3   \n",
       "16                            0                          0   \n",
       "17                            0                          0   \n",
       "18                            0                          0   \n",
       "19                            0                          1   \n",
       "\n",
       "    reservation_status reservation_status_date  id  \n",
       "0            Check-Out              2015-07-01   1  \n",
       "1            Check-Out              2015-07-01   2  \n",
       "2            Check-Out              2015-07-02   3  \n",
       "3            Check-Out              2015-07-02   4  \n",
       "4            Check-Out              2015-07-03   5  \n",
       "5            Check-Out              2015-07-03   6  \n",
       "6            Check-Out              2015-07-03   7  \n",
       "7            Check-Out              2015-07-03   8  \n",
       "8             Canceled              2015-05-06   9  \n",
       "9             Canceled              2015-04-22  10  \n",
       "10            Canceled              2015-06-23  11  \n",
       "11           Check-Out              2015-07-05  12  \n",
       "12           Check-Out              2015-07-05  13  \n",
       "13           Check-Out              2015-07-05  14  \n",
       "14           Check-Out              2015-07-05  15  \n",
       "15           Check-Out              2015-07-05  16  \n",
       "16           Check-Out              2015-07-05  17  \n",
       "17           Check-Out              2015-07-02  18  \n",
       "18           Check-Out              2015-07-02  19  \n",
       "19           Check-Out              2015-07-05  20  \n",
       "\n",
       "[20 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
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  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119390 entries, 0 to 119389\n",
      "Data columns (total 32 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   is_canceled                     119390 non-null  int64  \n",
      " 1   lead_time                       119390 non-null  int64  \n",
      " 2   arrival_date_year               119390 non-null  int64  \n",
      " 3   arrival_date_month              119390 non-null  object \n",
      " 4   arrival_date_week_number        119390 non-null  int64  \n",
      " 5   arrival_date_day_of_month       119390 non-null  int64  \n",
      " 6   stays_in_weekend_nights         119390 non-null  int64  \n",
      " 7   stays_in_week_nights            119390 non-null  int64  \n",
      " 8   adults                          119390 non-null  int64  \n",
      " 9   children                        119386 non-null  float64\n",
      " 10  babies                          119390 non-null  int64  \n",
      " 11  meal                            119390 non-null  object \n",
      " 12  country                         118902 non-null  object \n",
      " 13  market_segment                  119390 non-null  object \n",
      " 14  distribution_channel            119390 non-null  object \n",
      " 15  is_repeated_guest               119390 non-null  int64  \n",
      " 16  previous_cancellations          119390 non-null  int64  \n",
      " 17  previous_bookings_not_canceled  119390 non-null  int64  \n",
      " 18  reserved_room_type              119390 non-null  object \n",
      " 19  assigned_room_type              119390 non-null  object \n",
      " 20  booking_changes                 119390 non-null  int64  \n",
      " 21  deposit_type                    119390 non-null  object \n",
      " 22  agent                           119390 non-null  object \n",
      " 23  company                         119390 non-null  object \n",
      " 24  days_in_waiting_list            119390 non-null  int64  \n",
      " 25  customer_type                   119390 non-null  object \n",
      " 26  adr                             119390 non-null  float64\n",
      " 27  required_car_parking_spaces     119390 non-null  int64  \n",
      " 28  total_of_special_requests       119390 non-null  int64  \n",
      " 29  reservation_status              119390 non-null  object \n",
      " 30  reservation_status_date         119390 non-null  object \n",
      " 31  id                              119390 non-null  int64  \n",
      "dtypes: float64(2), int64(17), object(13)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
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  {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_canceled</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.370416</td>\n",
       "      <td>0.482918</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>104.011416</td>\n",
       "      <td>106.863097</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>69.000</td>\n",
       "      <td>160.00</td>\n",
       "      <td>737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>2016.156554</td>\n",
       "      <td>0.707476</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2016.000</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>27.165173</td>\n",
       "      <td>13.605138</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>28.000</td>\n",
       "      <td>38.00</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>15.798241</td>\n",
       "      <td>8.780829</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>16.000</td>\n",
       "      <td>23.00</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.927599</td>\n",
       "      <td>0.998613</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>2.500302</td>\n",
       "      <td>1.908286</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adults</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>1.856403</td>\n",
       "      <td>0.579261</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>119386.0</td>\n",
       "      <td>0.103890</td>\n",
       "      <td>0.398561</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.097436</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.031912</td>\n",
       "      <td>0.175767</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.087118</td>\n",
       "      <td>0.844336</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.137097</td>\n",
       "      <td>1.497437</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_changes</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.221124</td>\n",
       "      <td>0.652306</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>2.321149</td>\n",
       "      <td>17.594721</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>101.831122</td>\n",
       "      <td>50.535790</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>69.29</td>\n",
       "      <td>94.575</td>\n",
       "      <td>126.00</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.062518</td>\n",
       "      <td>0.245291</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>0.571363</td>\n",
       "      <td>0.792798</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>119390.0</td>\n",
       "      <td>59695.500000</td>\n",
       "      <td>34465.068657</td>\n",
       "      <td>1.00</td>\n",
       "      <td>29848.25</td>\n",
       "      <td>59695.500</td>\n",
       "      <td>89542.75</td>\n",
       "      <td>119390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                   count          mean           std      min  \\\n",
       "is_canceled                     119390.0      0.370416      0.482918     0.00   \n",
       "lead_time                       119390.0    104.011416    106.863097     0.00   \n",
       "arrival_date_year               119390.0   2016.156554      0.707476  2015.00   \n",
       "arrival_date_week_number        119390.0     27.165173     13.605138     1.00   \n",
       "arrival_date_day_of_month       119390.0     15.798241      8.780829     1.00   \n",
       "stays_in_weekend_nights         119390.0      0.927599      0.998613     0.00   \n",
       "stays_in_week_nights            119390.0      2.500302      1.908286     0.00   \n",
       "adults                          119390.0      1.856403      0.579261     0.00   \n",
       "children                        119386.0      0.103890      0.398561     0.00   \n",
       "babies                          119390.0      0.007949      0.097436     0.00   \n",
       "is_repeated_guest               119390.0      0.031912      0.175767     0.00   \n",
       "previous_cancellations          119390.0      0.087118      0.844336     0.00   \n",
       "previous_bookings_not_canceled  119390.0      0.137097      1.497437     0.00   \n",
       "booking_changes                 119390.0      0.221124      0.652306     0.00   \n",
       "days_in_waiting_list            119390.0      2.321149     17.594721     0.00   \n",
       "adr                             119390.0    101.831122     50.535790    -6.38   \n",
       "required_car_parking_spaces     119390.0      0.062518      0.245291     0.00   \n",
       "total_of_special_requests       119390.0      0.571363      0.792798     0.00   \n",
       "id                              119390.0  59695.500000  34465.068657     1.00   \n",
       "\n",
       "                                     25%        50%       75%       max  \n",
       "is_canceled                         0.00      0.000      1.00       1.0  \n",
       "lead_time                          18.00     69.000    160.00     737.0  \n",
       "arrival_date_year                2016.00   2016.000   2017.00    2017.0  \n",
       "arrival_date_week_number           16.00     28.000     38.00      53.0  \n",
       "arrival_date_day_of_month           8.00     16.000     23.00      31.0  \n",
       "stays_in_weekend_nights             0.00      1.000      2.00      19.0  \n",
       "stays_in_week_nights                1.00      2.000      3.00      50.0  \n",
       "adults                              2.00      2.000      2.00      55.0  \n",
       "children                            0.00      0.000      0.00      10.0  \n",
       "babies                              0.00      0.000      0.00      10.0  \n",
       "is_repeated_guest                   0.00      0.000      0.00       1.0  \n",
       "previous_cancellations              0.00      0.000      0.00      26.0  \n",
       "previous_bookings_not_canceled      0.00      0.000      0.00      72.0  \n",
       "booking_changes                     0.00      0.000      0.00      21.0  \n",
       "days_in_waiting_list                0.00      0.000      0.00     391.0  \n",
       "adr                                69.29     94.575    126.00    5400.0  \n",
       "required_car_parking_spaces         0.00      0.000      0.00       8.0  \n",
       "total_of_special_requests           0.00      0.000      1.00       5.0  \n",
       "id                              29848.25  59695.500  89542.75  119390.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "fpHkm6JaFKqT",
    "outputId": "b6dc67b8-a564-4ce3-a961-9b6d1e0a1eb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BB       ', 'FB       ', 'HB       ', 'SC       ', 'Undefined'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.meal.unique()\n",
    "# undefined/SC - No meal package\n",
    "# BB - Bed and Breakfast\n",
    "# HB = Half board (Breakfast and one other meal – usually dinner)\n",
    "# FB = Full board (Breakfast, lunch and dinner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "07M439ImFzAB",
    "outputId": "be61dced-af09-4448-8820-f0ab7629f1aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PRT', 'GBR', 'USA', 'ESP', 'IRL', 'FRA', nan, 'ROU', 'NOR', 'OMN',\n",
       "       'ARG', 'POL', 'DEU', 'BEL', 'CHE', 'CN', 'GRC', 'ITA', 'NLD',\n",
       "       'DNK', 'RUS', 'SWE', 'AUS', 'EST', 'CZE', 'BRA', 'FIN', 'MOZ',\n",
       "       'BWA', 'LUX', 'SVN', 'ALB', 'IND', 'CHN', 'MEX', 'MAR', 'UKR',\n",
       "       'SMR', 'LVA', 'PRI', 'SRB', 'CHL', 'AUT', 'BLR', 'LTU', 'TUR',\n",
       "       'ZAF', 'AGO', 'ISR', 'CYM', 'ZMB', 'CPV', 'ZWE', 'DZA', 'KOR',\n",
       "       'CRI', 'HUN', 'ARE', 'TUN', 'JAM', 'HRV', 'HKG', 'IRN', 'GEO',\n",
       "       'AND', 'GIB', 'URY', 'JEY', 'CAF', 'CYP', 'COL', 'GGY', 'KWT',\n",
       "       'NGA', 'MDV', 'VEN', 'SVK', 'FJI', 'KAZ', 'PAK', 'IDN', 'LBN',\n",
       "       'PHL', 'SEN', 'SYC', 'AZE', 'BHR', 'NZL', 'THA', 'DOM', 'MKD',\n",
       "       'MYS', 'ARM', 'JPN', 'LKA', 'CUB', 'CMR', 'BIH', 'MUS', 'COM',\n",
       "       'SUR', 'UGA', 'BGR', 'CIV', 'JOR', 'SYR', 'SGP', 'BDI', 'SAU',\n",
       "       'VNM', 'PLW', 'QAT', 'EGY', 'PER', 'MLT', 'MWI', 'ECU', 'MDG',\n",
       "       'ISL', 'UZB', 'NPL', 'BHS', 'MAC', 'TGO', 'TWN', 'DJI', 'STP',\n",
       "       'KNA', 'ETH', 'IRQ', 'HND', 'RWA', 'KHM', 'MCO', 'BGD', 'IMN',\n",
       "       'TJK', 'NIC', 'BEN', 'VGB', 'TZA', 'GAB', 'GHA', 'TMP', 'GLP',\n",
       "       'KEN', 'LIE', 'GNB', 'MNE', 'UMI', 'MYT', 'FRO', 'MMR', 'PAN',\n",
       "       'BFA', 'LBY', 'MLI', 'NAM', 'BOL', 'PRY', 'BRB', 'ABW', 'AIA',\n",
       "       'SLV', 'DMA', 'PYF', 'GUY', 'LCA', 'ATA', 'GTM', 'ASM', 'MRT',\n",
       "       'NCL', 'KIR', 'SDN', 'ATF', 'SLE', 'LAO'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ik4G6kxyFVyn",
    "outputId": "fceda4f0-ba11-46d9-f995-84baf378223f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Transient', 'Contract', 'Transient-Party', 'Group'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.customer_type.unique()\n",
    "# Contract: When the booking has an allotment or other type of contract associated to it\n",
    "# Group: When the booking is associated to a group\n",
    "# Transient: When the booking is not part of a group or contract, and is not associated to other transient booking\n",
    "# Transient-party: When the booking is transient, but is associated to at least other transient booking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "X0X6MytPFYIf"
   },
   "outputs": [],
   "source": [
    "#convert null or undefined string value to np.nan\n",
    "for col in df.columns:\n",
    "    if df[col].dtype == 'object' and col != 'country':\n",
    "        df.loc[df[col].str.contains('NULL'), col] = np.nan\n",
    "        df.loc[df[col].str.contains('Undefined', na=False), col] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "E2xZPI94e5Qr",
    "outputId": "40d1b50e-8ecf-42f2-a6f8-06e162f3fafa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_canceled                            0\n",
       "lead_time                              0\n",
       "arrival_date_year                      0\n",
       "arrival_date_month                     0\n",
       "arrival_date_week_number               0\n",
       "arrival_date_day_of_month              0\n",
       "stays_in_weekend_nights                0\n",
       "stays_in_week_nights                   0\n",
       "adults                                 0\n",
       "children                               4\n",
       "babies                                 0\n",
       "meal                                1169\n",
       "country                              488\n",
       "market_segment                         2\n",
       "distribution_channel                   5\n",
       "is_repeated_guest                      0\n",
       "previous_cancellations                 0\n",
       "previous_bookings_not_canceled         0\n",
       "reserved_room_type                     0\n",
       "assigned_room_type                     0\n",
       "booking_changes                        0\n",
       "deposit_type                           0\n",
       "agent                              16340\n",
       "company                           112593\n",
       "days_in_waiting_list                   0\n",
       "customer_type                          0\n",
       "adr                                    0\n",
       "required_car_parking_spaces            0\n",
       "total_of_special_requests              0\n",
       "reservation_status                     0\n",
       "reservation_status_date                0\n",
       "id                                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "kIP8vcRWfjK-"
   },
   "outputs": [],
   "source": [
    "#Delete Some missing values\n",
    "subset = [\n",
    "    'country', \n",
    "    'children', \n",
    "    'market_segment', \n",
    "    'distribution_channel',\n",
    "]\n",
    "df = df.dropna(subset=subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "mDaIVhtAfmf7"
   },
   "outputs": [],
   "source": [
    "#some missing value may not be useful , so inserting some dummy value\n",
    "df.loc[df.agent.isnull(), 'agent'] = 999\n",
    "df.loc[df.company.isnull(), 'company'] = 999\n",
    "df.loc[df.meal.isnull(), 'meal'] = 'SC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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    },
    "id": "K_XLemQghM_z",
    "outputId": "1d997735-f381-4f62-8efb-31dd13d92f20"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
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       "      <th>is_canceled</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.371355</td>\n",
       "      <td>0.483169</td>\n",
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       "      <td>118897.0</td>\n",
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       "      <td>737.0</td>\n",
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       "      <th>arrival_date_year</th>\n",
       "      <td>118897.0</td>\n",
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       "      <td>2017.0</td>\n",
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       "      <th>arrival_date_week_number</th>\n",
       "      <td>118897.0</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.0</td>\n",
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       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>15.800971</td>\n",
       "      <td>8.780305</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
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       "      <th>stays_in_weekend_nights</th>\n",
       "      <td>118897.0</td>\n",
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       "      <td>0.996215</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
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       "    <tr>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>2.502141</td>\n",
       "      <td>1.900175</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adults</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>1.858390</td>\n",
       "      <td>0.578578</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.104199</td>\n",
       "      <td>0.399166</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>babies</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.007948</td>\n",
       "      <td>0.097381</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.032011</td>\n",
       "      <td>0.176030</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.087143</td>\n",
       "      <td>0.845872</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>118897.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>booking_changes</th>\n",
       "      <td>118897.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>118897.0</td>\n",
       "      <td>102.003153</td>\n",
       "      <td>50.486065</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5400.0</td>\n",
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       "      <th>required_car_parking_spaces</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.061877</td>\n",
       "      <td>0.244158</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>118897.0</td>\n",
       "      <td>0.571671</td>\n",
       "      <td>0.792670</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "                                   count          mean           std      min  \\\n",
       "is_canceled                     118897.0      0.371355      0.483169     0.00   \n",
       "lead_time                       118897.0    104.311446    106.903759     0.00   \n",
       "arrival_date_year               118897.0   2016.157666      0.707454  2015.00   \n",
       "arrival_date_week_number        118897.0     27.166548     13.590028     1.00   \n",
       "arrival_date_day_of_month       118897.0     15.800971      8.780305     1.00   \n",
       "stays_in_weekend_nights         118897.0      0.928888      0.996215     0.00   \n",
       "stays_in_week_nights            118897.0      2.502141      1.900175     0.00   \n",
       "adults                          118897.0      1.858390      0.578578     0.00   \n",
       "children                        118897.0      0.104199      0.399166     0.00   \n",
       "babies                          118897.0      0.007948      0.097381     0.00   \n",
       "is_repeated_guest               118897.0      0.032011      0.176030     0.00   \n",
       "previous_cancellations          118897.0      0.087143      0.845872     0.00   \n",
       "previous_bookings_not_canceled  118897.0      0.131635      1.484678     0.00   \n",
       "booking_changes                 118897.0      0.221183      0.652787     0.00   \n",
       "days_in_waiting_list            118897.0      2.330774     17.630525     0.00   \n",
       "adr                             118897.0    102.003153     50.486065    -6.38   \n",
       "required_car_parking_spaces     118897.0      0.061877      0.244158     0.00   \n",
       "total_of_special_requests       118897.0      0.571671      0.792670     0.00   \n",
       "id                              118897.0  59874.165320  34418.603203     1.00   \n",
       "\n",
       "                                    25%      50%      75%       max  \n",
       "is_canceled                         0.0      0.0      1.0       1.0  \n",
       "lead_time                          18.0     69.0    161.0     737.0  \n",
       "arrival_date_year                2016.0   2016.0   2017.0    2017.0  \n",
       "arrival_date_week_number           16.0     28.0     38.0      53.0  \n",
       "arrival_date_day_of_month           8.0     16.0     23.0      31.0  \n",
       "stays_in_weekend_nights             0.0      1.0      2.0      16.0  \n",
       "stays_in_week_nights                1.0      2.0      3.0      41.0  \n",
       "adults                              2.0      2.0      2.0      55.0  \n",
       "children                            0.0      0.0      0.0      10.0  \n",
       "babies                              0.0      0.0      0.0      10.0  \n",
       "is_repeated_guest                   0.0      0.0      0.0       1.0  \n",
       "previous_cancellations              0.0      0.0      0.0      26.0  \n",
       "previous_bookings_not_canceled      0.0      0.0      0.0      72.0  \n",
       "booking_changes                     0.0      0.0      0.0      21.0  \n",
       "days_in_waiting_list                0.0      0.0      0.0     391.0  \n",
       "adr                                70.0     95.0    126.0    5400.0  \n",
       "required_car_parking_spaces         0.0      0.0      0.0       8.0  \n",
       "total_of_special_requests           0.0      0.0      1.0       5.0  \n",
       "id                              30187.0  59932.0  89665.0  119390.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "jXywpWTxhtWw"
   },
   "outputs": [],
   "source": [
    "# ADR field refers to the average price per night of the reservation. \n",
    "#Therefore, it is not normal for it to take a value smaller than zero. \n",
    "#After using df.describe().T to see such situations. We delete values that are smaller than zero for the ADR field.\n",
    "df = df[df.adr > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "AoXerdNZhyAv"
   },
   "outputs": [],
   "source": [
    "df[[\"meal\", \"reserved_room_type\", \"market_segment\", \"reservation_status\",\"agent\",\"company\"]] = \\\n",
    "df[[\"meal\", \"reserved_room_type\", \"market_segment\", \"reservation_status\",\"agent\",\"company\"]].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "97tc5zsVi0z-",
    "outputId": "971f442e-eea6-4ad7-9020-158527e0927e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['meal', 'market_segment', 'reserved_room_type', 'agent', 'company',\n",
       "       'reservation_status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_columns = df.describe(include=\"category\").columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ThGqcgukji3V",
    "outputId": "322a6fe8-963f-467c-b174-6ca38e85fe35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique values in meal are:\n",
      "BB           90376\n",
      "HB           14232\n",
      "SC           10440\n",
      "SC            1128\n",
      "FB             782\n",
      "Name: meal, dtype: int64\n",
      "**************************************************\n",
      "unique values in market_segment are:\n",
      "Online TA        56036\n",
      "Offline TA/TO    23830\n",
      "Groups           19554\n",
      "Direct           12209\n",
      "Corporate         5035\n",
      "Aviation           231\n",
      "Complementary       63\n",
      "Name: market_segment, dtype: int64\n",
      "**************************************************\n",
      "unique values in reserved_room_type are:\n",
      "A                   84189\n",
      "D                   18977\n",
      "E                    6385\n",
      "F                    2817\n",
      "G                    1995\n",
      "B                    1081\n",
      "C                     913\n",
      "H                     595\n",
      "L                       6\n",
      "Name: reserved_room_type, dtype: int64\n",
      "**************************************************\n",
      "unique values in agent are:\n",
      "          9    31856\n",
      "999            15073\n",
      "        240    13737\n",
      "          1     7078\n",
      "         14     3619\n",
      "               ...  \n",
      "         70        1\n",
      "        283        1\n",
      "        285        1\n",
      "         93        1\n",
      "        433        1\n",
      "Name: agent, Length: 332, dtype: int64\n",
      "**************************************************\n",
      "unique values in company are:\n",
      "999            110736\n",
      "         40       920\n",
      "        223       771\n",
      "         67       267\n",
      "        153       208\n",
      "                ...  \n",
      "        160         1\n",
      "        184         1\n",
      "        402         1\n",
      "        401         1\n",
      "        419         1\n",
      "Name: company, Length: 343, dtype: int64\n",
      "**************************************************\n",
      "unique values in reservation_status are:\n",
      "Check-Out    73008\n",
      "Canceled     42773\n",
      "No-Show       1177\n",
      "Name: reservation_status, dtype: int64\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "for i in cat_columns:\n",
    "    print(\"unique values in\", i, \"are:\")\n",
    "    print(df[i].value_counts())\n",
    "    print(\"*\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "AQd-Lh7Kjov5",
    "outputId": "f4ce8ee4-830d-41c7-f65b-7580270739cf"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_canceled</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.375776</td>\n",
       "      <td>0.484325</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>105.376297</td>\n",
       "      <td>106.944554</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>71.0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>2016.161451</td>\n",
       "      <td>0.706501</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>27.137981</td>\n",
       "      <td>13.561225</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>15.805221</td>\n",
       "      <td>8.783034</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.937670</td>\n",
       "      <td>0.993328</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>2.522957</td>\n",
       "      <td>1.885579</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adults</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>1.862438</td>\n",
       "      <td>0.481129</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.104815</td>\n",
       "      <td>0.400288</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.097102</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.027959</td>\n",
       "      <td>0.164855</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.086963</td>\n",
       "      <td>0.850186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.119795</td>\n",
       "      <td>1.432435</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_changes</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.216514</td>\n",
       "      <td>0.633573</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>2.347373</td>\n",
       "      <td>17.712061</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>103.694277</td>\n",
       "      <td>49.150097</td>\n",
       "      <td>0.26</td>\n",
       "      <td>71.00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>126.00</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.061988</td>\n",
       "      <td>0.244410</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>0.571256</td>\n",
       "      <td>0.791486</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>116958.0</td>\n",
       "      <td>59883.273406</td>\n",
       "      <td>34406.925952</td>\n",
       "      <td>3.00</td>\n",
       "      <td>30252.25</td>\n",
       "      <td>59904.5</td>\n",
       "      <td>89680.75</td>\n",
       "      <td>119390.0</td>\n",
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      "text/plain": [
       "                                   count          mean           std      min  \\\n",
       "is_canceled                     116958.0      0.375776      0.484325     0.00   \n",
       "lead_time                       116958.0    105.376297    106.944554     0.00   \n",
       "arrival_date_year               116958.0   2016.161451      0.706501  2015.00   \n",
       "arrival_date_week_number        116958.0     27.137981     13.561225     1.00   \n",
       "arrival_date_day_of_month       116958.0     15.805221      8.783034     1.00   \n",
       "stays_in_weekend_nights         116958.0      0.937670      0.993328     0.00   \n",
       "stays_in_week_nights            116958.0      2.522957      1.885579     0.00   \n",
       "adults                          116958.0      1.862438      0.481129     0.00   \n",
       "children                        116958.0      0.104815      0.400288     0.00   \n",
       "babies                          116958.0      0.007866      0.097102     0.00   \n",
       "is_repeated_guest               116958.0      0.027959      0.164855     0.00   \n",
       "previous_cancellations          116958.0      0.086963      0.850186     0.00   \n",
       "previous_bookings_not_canceled  116958.0      0.119795      1.432435     0.00   \n",
       "booking_changes                 116958.0      0.216514      0.633573     0.00   \n",
       "days_in_waiting_list            116958.0      2.347373     17.712061     0.00   \n",
       "adr                             116958.0    103.694277     49.150097     0.26   \n",
       "required_car_parking_spaces     116958.0      0.061988      0.244410     0.00   \n",
       "total_of_special_requests       116958.0      0.571256      0.791486     0.00   \n",
       "id                              116958.0  59883.273406  34406.925952     3.00   \n",
       "\n",
       "                                     25%      50%       75%       max  \n",
       "is_canceled                         0.00      0.0      1.00       1.0  \n",
       "lead_time                          19.00     71.0    162.00     709.0  \n",
       "arrival_date_year                2016.00   2016.0   2017.00    2017.0  \n",
       "arrival_date_week_number           16.00     27.0     38.00      53.0  \n",
       "arrival_date_day_of_month           8.00     16.0     23.00      31.0  \n",
       "stays_in_weekend_nights             0.00      1.0      2.00      16.0  \n",
       "stays_in_week_nights                1.00      2.0      3.00      41.0  \n",
       "adults                              2.00      2.0      2.00       4.0  \n",
       "children                            0.00      0.0      0.00      10.0  \n",
       "babies                              0.00      0.0      0.00      10.0  \n",
       "is_repeated_guest                   0.00      0.0      0.00       1.0  \n",
       "previous_cancellations              0.00      0.0      0.00      26.0  \n",
       "previous_bookings_not_canceled      0.00      0.0      0.00      72.0  \n",
       "booking_changes                     0.00      0.0      0.00      18.0  \n",
       "days_in_waiting_list                0.00      0.0      0.00     391.0  \n",
       "adr                                71.00     95.0    126.00    5400.0  \n",
       "required_car_parking_spaces         0.00      0.0      0.00       8.0  \n",
       "total_of_special_requests           0.00      0.0      1.00       5.0  \n",
       "id                              30252.25  59904.5  89680.75  119390.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "J5-912HWOXbt"
   },
   "outputs": [],
   "source": [
    "#outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BljB7QRsAJxD",
    "outputId": "8598ecff-f40c-428a-862c-70cd556bbed5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower point: -195.5 \t upper point: 376.5 \t lead_time\n",
      "Lower point: -3.0 \t upper point: 5.0 \t stays_in_weekend_nights\n",
      "Lower point: -2.0 \t upper point: 6.0 \t stays_in_week_nights\n",
      "Lower point: 2.0 \t upper point: 2.0 \t adults\n",
      "Lower point: 0.0 \t upper point: 0.0 \t children\n",
      "Lower point: 0.0 \t upper point: 0.0 \t babies\n",
      "Lower point: -11.5 \t upper point: 208.5 \t adr\n"
     ]
    }
   ],
   "source": [
    "#InterQuartile Rule\n",
    "\n",
    "cleaned = df.copy()\n",
    "\n",
    "columns = [\n",
    "    'lead_time',\n",
    "    'stays_in_weekend_nights',\n",
    "    'stays_in_week_nights',\n",
    "    'adults',\n",
    "    'children',\n",
    "    'babies',\n",
    "    'adr',\n",
    "]\n",
    "\n",
    "for col in columns:\n",
    "    q1 = df[col].quantile(0.25)\n",
    "    q3 = df[col].quantile(0.75)\n",
    "\n",
    "    iqr = q3 - q1\n",
    "    lower = q1 - 1.5 * iqr\n",
    "    upper = q3 + 1.5 * iqr\n",
    "\n",
    "    print(f'Lower point: {round(lower, 2)} \\t upper point: {round(upper, 2)} \\t {col}')\n",
    "    \n",
    "    if lower == upper:\n",
    "        continue\n",
    "    \n",
    "    cond1 = (cleaned[col] >= lower) & (cleaned[col] <= upper)\n",
    "    cond2 = cleaned[col].isnull()\n",
    "    cleaned = cleaned[cond1 | cond2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "vgstIfhvA1YZ",
    "outputId": "3a680918-cb2a-4a59-d347-c7e44cbf7175"
   },
   "outputs": [
    {
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       "      <td>96.088406</td>\n",
       "      <td>92.822540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>106723.0</td>\n",
       "      <td>0.083815</td>\n",
       "      <td>0.350265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>106723.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>106723.0</td>\n",
       "      <td>99.155196</td>\n",
       "      <td>38.421950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>94.8</td>\n",
       "      <td>123.14</td>\n",
       "      <td>208.5</td>\n",
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      "text/plain": [
       "                            count       mean        std  min   25%   50%  \\\n",
       "lead_time                106723.0  96.088406  92.822540  0.0  18.0  67.0   \n",
       "stays_in_weekend_nights  106723.0   0.865062   0.870129  0.0   0.0   1.0   \n",
       "stays_in_week_nights     106723.0   2.318029   1.415296  0.0   1.0   2.0   \n",
       "adults                   106723.0   1.848083   0.482426  0.0   2.0   2.0   \n",
       "children                 106723.0   0.083815   0.350265  0.0   0.0   0.0   \n",
       "babies                   106723.0   0.007534   0.095824  0.0   0.0   0.0   \n",
       "adr                      106723.0  99.155196  38.421950  1.0  71.1  94.8   \n",
       "\n",
       "                            75%    max  \n",
       "lead_time                152.00  374.0  \n",
       "stays_in_weekend_nights    2.00    4.0  \n",
       "stays_in_week_nights       3.00    6.0  \n",
       "adults                     2.00    4.0  \n",
       "children                   0.00    3.0  \n",
       "babies                     0.00   10.0  \n",
       "adr                      123.14  208.5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned[columns].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "9SFAbD96A5Km"
   },
   "outputs": [],
   "source": [
    "# Multivariate Anomaly Detection\n",
    "  # Some observations may not be outlier when alone\n",
    "  # However, when combined with another variable, outlier values may occur\n",
    "  # For example, with the price - nights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "hULRRWnNBuvI"
   },
   "outputs": [],
   "source": [
    "# thats not important, just for visualization\n",
    "def display_side_by_side(*args):\n",
    "    html_str = ''\n",
    "    \n",
    "    for df in args:\n",
    "        html_str += df.to_html()\n",
    "        \n",
    "    display_html(html_str.replace('table', 'table style=\"display:inline\"'), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "rGhWqSI9B5tk"
   },
   "outputs": [],
   "source": [
    "cleaned.loc[:, 'total_nights'] = cleaned['stays_in_week_nights'] + cleaned['stays_in_weekend_nights']\n",
    "cleaned.loc[:, 'price'] = cleaned['adr'] * cleaned['total_nights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "gtxeb_E3CAEj"
   },
   "outputs": [],
   "source": [
    "cols = ['total_nights', 'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "_iO6xK0dCCHy"
   },
   "outputs": [],
   "source": [
    "# convert to numpy array, because local outlier method want this\n",
    "X = np.array(cleaned[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "ARM8d0qwCD9O"
   },
   "outputs": [],
   "source": [
    "#Elliptice Envelope\n",
    "\n",
    "# create model\n",
    "ee = EllipticEnvelope(contamination=.01, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "wkIp1URiCNTW"
   },
   "outputs": [],
   "source": [
    "# predictions\n",
    "y_pred_ee = ee.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "vzB_MVGgCRvg"
   },
   "outputs": [],
   "source": [
    "# predictions (-1: outlier, 1: normal)\n",
    "anomalies = X[y_pred_ee == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "id": "hPXKqeKLCUEq",
    "outputId": "7feceb1f-e6d3-416d-f056-a2e47656860f"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data and outliers\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.scatter(X[:, 0], X[:, 1], c='white', s=20, edgecolor='k')\n",
    "plt.scatter(anomalies[:, 0], anomalies[:, 1], c='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "P2fwlpUHCXDD"
   },
   "outputs": [],
   "source": [
    "# statistics of outlier observations\n",
    "outliers = cleaned[y_pred_ee == -1][cols].groupby(cols[0]).agg({cols[1]: ['min', 'max', 'mean', 'std', 'median']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "de4Cq8ruCtgI"
   },
   "outputs": [],
   "source": [
    "# statistics of all observations\n",
    "all_data = cleaned[cols].groupby(cols[0]).agg({cols[1]: ['min', 'max', 'mean', 'std', 'median']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "00fnBrv7CvRJ"
   },
   "outputs": [],
   "source": [
    "# statistics of cleaned data\n",
    "clean_data = cleaned[y_pred_ee != -1].groupby(cols[0]).agg({cols[1]: ['min', 'max', 'mean', 'std', 'median']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "UoZCIpWdCwyz",
    "outputId": "a7f101dd-644d-42bf-8d0b-3a6934885fd7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>208.00</td>\n",
       "      <td>93.551632</td>\n",
       "      <td>40.041280</td>\n",
       "      <td>89.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>417.00</td>\n",
       "      <td>193.363506</td>\n",
       "      <td>71.809371</td>\n",
       "      <td>187.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.00</td>\n",
       "      <td>624.99</td>\n",
       "      <td>307.502708</td>\n",
       "      <td>108.518262</td>\n",
       "      <td>291.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>834.00</td>\n",
       "      <td>402.891749</td>\n",
       "      <td>149.940802</td>\n",
       "      <td>384.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.00</td>\n",
       "      <td>1040.00</td>\n",
       "      <td>528.015487</td>\n",
       "      <td>196.280852</td>\n",
       "      <td>501.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36.00</td>\n",
       "      <td>1249.02</td>\n",
       "      <td>655.874750</td>\n",
       "      <td>249.239448</td>\n",
       "      <td>635.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.03</td>\n",
       "      <td>1459.36</td>\n",
       "      <td>666.379208</td>\n",
       "      <td>305.362646</td>\n",
       "      <td>598.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.00</td>\n",
       "      <td>1664.16</td>\n",
       "      <td>832.900760</td>\n",
       "      <td>362.480690</td>\n",
       "      <td>795.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.04</td>\n",
       "      <td>1867.50</td>\n",
       "      <td>1002.631601</td>\n",
       "      <td>409.619361</td>\n",
       "      <td>909.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.00</td>\n",
       "      <td>2082.00</td>\n",
       "      <td>993.092523</td>\n",
       "      <td>418.914945</td>\n",
       "      <td>921.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1152.00</td>\n",
       "      <td>1249.02</td>\n",
       "      <td>1196.127231</td>\n",
       "      <td>28.867197</td>\n",
       "      <td>1193.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.03</td>\n",
       "      <td>1459.36</td>\n",
       "      <td>1314.514600</td>\n",
       "      <td>142.000417</td>\n",
       "      <td>1320.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.00</td>\n",
       "      <td>1664.16</td>\n",
       "      <td>1400.684800</td>\n",
       "      <td>299.490422</td>\n",
       "      <td>1450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.04</td>\n",
       "      <td>1867.50</td>\n",
       "      <td>1413.963679</td>\n",
       "      <td>445.921108</td>\n",
       "      <td>1480.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.00</td>\n",
       "      <td>2082.00</td>\n",
       "      <td>1267.973000</td>\n",
       "      <td>584.023093</td>\n",
       "      <td>1474.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>208.00</td>\n",
       "      <td>93.551632</td>\n",
       "      <td>40.041280</td>\n",
       "      <td>89.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>417.00</td>\n",
       "      <td>193.363506</td>\n",
       "      <td>71.809371</td>\n",
       "      <td>187.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>624.99</td>\n",
       "      <td>307.502708</td>\n",
       "      <td>108.518262</td>\n",
       "      <td>291.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>834.00</td>\n",
       "      <td>402.891749</td>\n",
       "      <td>149.940802</td>\n",
       "      <td>384.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1040.00</td>\n",
       "      <td>528.015487</td>\n",
       "      <td>196.280852</td>\n",
       "      <td>501.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36.0</td>\n",
       "      <td>1150.02</td>\n",
       "      <td>634.897204</td>\n",
       "      <td>229.617591</td>\n",
       "      <td>621.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1210.02</td>\n",
       "      <td>618.868283</td>\n",
       "      <td>256.030785</td>\n",
       "      <td>573.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>208.8</td>\n",
       "      <td>1260.88</td>\n",
       "      <td>738.479911</td>\n",
       "      <td>275.387665</td>\n",
       "      <td>720.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>324.0</td>\n",
       "      <td>1296.00</td>\n",
       "      <td>828.226800</td>\n",
       "      <td>229.781849</td>\n",
       "      <td>801.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>480.0</td>\n",
       "      <td>1322.40</td>\n",
       "      <td>868.712217</td>\n",
       "      <td>227.781794</td>\n",
       "      <td>840.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_side_by_side(all_data, outliers, clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "ASO2tlJUDloW"
   },
   "outputs": [],
   "source": [
    "df_cleaned = cleaned[y_pred_ee != -1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "V9L4nIUZDnXv"
   },
   "outputs": [],
   "source": [
    "h1_cleaned = df_cleaned[df_cleaned.id.isin(h1.id.tolist())]\n",
    "h2_cleaned = df_cleaned[df_cleaned.id.isin(h2.id.tolist())]\n",
    "\n",
    "h1_cleaned = h1_cleaned.drop('id', axis=1)\n",
    "h2_cleaned = h2_cleaned.drop('id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "kiXEMhw1Dpm7"
   },
   "outputs": [],
   "source": [
    "h1_cleaned.to_csv('/content/gdrive/MyDrive/Colab Notebooks/hbc/H1_cleaned.csv', index=False)\n",
    "h2_cleaned.to_csv('/content/gdrive/MyDrive/Colab Notebooks/hbc/H2_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XTlGTPpKDtsp"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Yr3D_eTjD2TK"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "hotel_booking_cancellation_preprocessing.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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