Nazril_Daniel_Hotel_Booking_Demand_Analysis_&_Cancelation_prediction_final version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbf64a71",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fe19e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\flame\\anaconda3\\lib\\site-packages (5.6.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\flame\\anaconda3\\lib\\site-packages (from plotly) (8.0.1)\n",
      "Requirement already satisfied: six in c:\\users\\flame\\anaconda3\\lib\\site-packages (from plotly) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "774625a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "# Data Processing\n",
    "import pandas as pd\n",
    "\n",
    "# Data Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216333e9",
   "metadata": {},
   "source": [
    "# Import data from Github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eec20925",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "url = 'https://github.com/Virementz/Hotel-Booking-Demand-Analysis-and-Cancelation-prediction/raw/main/hotel_bookings.csv'\n",
    "df = pd.read_csv(url)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f973ca1f",
   "metadata": {},
   "source": [
    "# Data Reading, Understanding & Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ded8aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</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>...</th>\n",
       "      <th>deposit_type</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</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",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\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",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\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>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\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>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\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>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               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",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
       "0                        0                     0       2  ...    No Deposit   \n",
       "1                        0                     0       2  ...    No Deposit   \n",
       "2                        0                     1       1  ...    No Deposit   \n",
       "3                        0                     1       1  ...    No Deposit   \n",
       "4                        0                     2       2  ...    No Deposit   \n",
       "\n",
       "   agent company days_in_waiting_list customer_type   adr  \\\n",
       "0    NaN     NaN                    0     Transient   0.0   \n",
       "1    NaN     NaN                    0     Transient   0.0   \n",
       "2    NaN     NaN                    0     Transient  75.0   \n",
       "3  304.0     NaN                    0     Transient  75.0   \n",
       "4  240.0     NaN                    0     Transient  98.0   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
       "0                            0                          0           Check-Out   \n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "3                            0                          0           Check-Out   \n",
       "4                            0                          1           Check-Out   \n",
       "\n",
       "  reservation_status_date  \n",
       "0              2015-07-01  \n",
       "1              2015-07-01  \n",
       "2              2015-07-02  \n",
       "3              2015-07-02  \n",
       "4              2015-07-03  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "812c53dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',\n",
       "       'arrival_date_month', 'arrival_date_week_number',\n",
       "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
       "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
       "       'country', 'market_segment', 'distribution_channel',\n",
       "       'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
       "       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',\n",
       "       'company', 'days_in_waiting_list', 'customer_type', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests',\n",
       "       'reservation_status', 'reservation_status_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09734c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c26a1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87396, 32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b67c5dd",
   "metadata": {},
   "source": [
    "Duplicated data has been drop from the data set to ensure a more precise model and eliminate outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "074d5868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 87396 entries, 0 to 119389\n",
      "Data columns (total 32 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   hotel                           87396 non-null  object \n",
      " 1   is_canceled                     87396 non-null  int64  \n",
      " 2   lead_time                       87396 non-null  int64  \n",
      " 3   arrival_date_year               87396 non-null  int64  \n",
      " 4   arrival_date_month              87396 non-null  object \n",
      " 5   arrival_date_week_number        87396 non-null  int64  \n",
      " 6   arrival_date_day_of_month       87396 non-null  int64  \n",
      " 7   stays_in_weekend_nights         87396 non-null  int64  \n",
      " 8   stays_in_week_nights            87396 non-null  int64  \n",
      " 9   adults                          87396 non-null  int64  \n",
      " 10  children                        87392 non-null  float64\n",
      " 11  babies                          87396 non-null  int64  \n",
      " 12  meal                            87396 non-null  object \n",
      " 13  country                         86944 non-null  object \n",
      " 14  market_segment                  87396 non-null  object \n",
      " 15  distribution_channel            87396 non-null  object \n",
      " 16  is_repeated_guest               87396 non-null  int64  \n",
      " 17  previous_cancellations          87396 non-null  int64  \n",
      " 18  previous_bookings_not_canceled  87396 non-null  int64  \n",
      " 19  reserved_room_type              87396 non-null  object \n",
      " 20  assigned_room_type              87396 non-null  object \n",
      " 21  booking_changes                 87396 non-null  int64  \n",
      " 22  deposit_type                    87396 non-null  object \n",
      " 23  agent                           75203 non-null  float64\n",
      " 24  company                         5259 non-null   float64\n",
      " 25  days_in_waiting_list            87396 non-null  int64  \n",
      " 26  customer_type                   87396 non-null  object \n",
      " 27  adr                             87396 non-null  float64\n",
      " 28  required_car_parking_spaces     87396 non-null  int64  \n",
      " 29  total_of_special_requests       87396 non-null  int64  \n",
      " 30  reservation_status              87396 non-null  object \n",
      " 31  reservation_status_date         87396 non-null  object \n",
      "dtypes: float64(4), int64(16), object(12)\n",
      "memory usage: 22.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be893f3",
   "metadata": {},
   "source": [
    "as what can be seen from the info above, some dataset will need to be change into another datatype for a more convenient computation and classification. for example, we will change datatype of children from float to integer, while the agent from float to string, while at the same time, removing any null values (if any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e8c92c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                                 0\n",
       "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                                  0\n",
       "country                             452\n",
       "market_segment                        0\n",
       "distribution_channel                  0\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                             12193\n",
       "company                           82137\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",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f57b95",
   "metadata": {},
   "source": [
    "company feature has many null values. So this column can be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4907a921",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns='company')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d515c6c",
   "metadata": {},
   "source": [
    "For agent feature, we assume that null value means booking is done by guest themselves. So first we will look through unique values in agent feature then assign null values with a different value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dab55980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan, 304., 240., 303.,  15., 241.,   8., 250., 115.,   5., 175.,\n",
       "       134., 156., 243., 242.,   3., 105.,  40., 147., 306., 184.,  96.,\n",
       "         2., 127.,  95., 146.,   9., 177.,   6., 143., 244., 149., 167.,\n",
       "       300., 171., 305.,  67., 196., 152., 142., 261., 104.,  36.,  26.,\n",
       "        29., 258., 110.,  71., 181.,  88., 251., 275.,  69., 248., 208.,\n",
       "       256., 314., 126., 281., 273., 253., 185., 330., 334., 328., 326.,\n",
       "       321., 324., 313.,  38., 155.,  68., 335., 308., 332.,  94., 348.,\n",
       "       310., 339., 375.,  66., 327., 387., 298.,  91., 245., 385., 257.,\n",
       "       393., 168., 405., 249., 315.,  75., 128., 307.,  11., 436.,   1.,\n",
       "       201., 183., 223., 368., 336., 291., 464., 411., 481.,  10., 154.,\n",
       "       468., 410., 390., 440., 495., 492., 493., 434.,  57., 531., 420.,\n",
       "       483., 526., 472., 429.,  16., 446.,  34.,  78., 139., 252., 270.,\n",
       "        47., 114., 301., 193., 182., 135., 350., 195., 352., 355., 159.,\n",
       "       363., 384., 360., 331., 367.,  64., 406., 163., 414., 333., 427.,\n",
       "       431., 430., 426., 438., 433., 418., 441., 282., 432.,  72., 450.,\n",
       "       180., 454., 455.,  59., 451., 254., 358., 469., 165., 467., 510.,\n",
       "       337., 476., 502., 527., 479., 508., 535., 302., 497., 187.,  13.,\n",
       "         7.,  27.,  14.,  22.,  17.,  28.,  42.,  20.,  19.,  45.,  37.,\n",
       "        61.,  39.,  21.,  24.,  41.,  50.,  30.,  54.,  52.,  12.,  44.,\n",
       "        31.,  83.,  32.,  63.,  60.,  55.,  56.,  89.,  87., 118.,  86.,\n",
       "        85., 210., 214., 129., 179., 138., 174., 170., 153.,  93., 151.,\n",
       "       119.,  35., 173.,  58.,  53., 133.,  79., 235., 192., 191., 236.,\n",
       "       162., 215., 157., 287., 132., 234.,  98.,  77., 103., 107., 262.,\n",
       "       220., 121., 205., 378.,  23., 296., 290., 229.,  33., 286., 276.,\n",
       "       425., 484., 323., 403., 219., 394., 509., 111., 423.,   4.,  70.,\n",
       "        82.,  81.,  74.,  92.,  99.,  90., 112., 117., 106., 148., 158.,\n",
       "       144., 211., 213., 216., 232., 150., 267., 227., 247., 278., 280.,\n",
       "       285., 289., 269., 295., 265., 288., 122., 294., 325., 341., 344.,\n",
       "       346., 359., 283., 364., 370., 371.,  25., 141., 391., 397., 416.,\n",
       "       404., 299., 197.,  73., 354., 444., 408., 461., 388., 453., 459.,\n",
       "       474., 475., 480., 449.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agent.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2998cbf",
   "metadata": {},
   "source": [
    "from the output above, we can see that there is no agent data segment that has value of 0. However, it is still good to double check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0b5a418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</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>...</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>agent</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [hotel, 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, days_in_waiting_list, customer_type, adr, required_car_parking_spaces, total_of_special_requests, reservation_status, reservation_status_date]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['agent']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42173340",
   "metadata": {},
   "source": [
    "no agent value that is equal to 0. Now we need to replace null values with 0 in agent feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed9d369d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['agent'].fillna(value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4935d8e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To remove rows with null value\n",
    "df = df.dropna(axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "017d4889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             0\n",
       "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                          0\n",
       "babies                            0\n",
       "meal                              0\n",
       "country                           0\n",
       "market_segment                    0\n",
       "distribution_channel              0\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                             0\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",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de02715",
   "metadata": {},
   "source": [
    "Now there are no null values.\n",
    "Next step is to look for unique values in each feature by looping through features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d4639b2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hotel has ['Resort Hotel' 'City Hotel'] \n",
      "\n",
      "is_canceled has [0 1] \n",
      "\n",
      "lead_time has [342 737   7  13  14   0   9  85  75  23  35  68  18  37  12  72 127  78\n",
      "  48  60  77  99  95  96  69  45  40  15  36  43  70  16 107  47 113  90\n",
      "  50  93  76   3   1  10   5  17  51  71  63  62 101   2  81 368 364 324\n",
      "  79  21 109 102   4  98  92  26  73 115  86  52  29  30  33  32   8 100\n",
      "  44  80  97  64  39  34  27  82  94 110 111  84  66 104  28 258 112  65\n",
      "  67  55  88  54 292  83 105 280 394  24 103 366 249  22  91  11 108 106\n",
      "  31  87  41 304 117  59  53  58 116  42 321  38 118  56  49 317   6  57\n",
      "  19  25 315 123  46  89  61 312 299 130  74 298 119  20 286 136 129 124\n",
      " 327 131 460 140 114 139 122 137 126 120 128 135 150 143 151 132 125 157\n",
      " 147 138 156 164 346 159 160 161 333 381 149 154 297 163 314 155 323 340\n",
      " 356 142 328 144 336 248 302 175 344 382 146 170 166 338 167 310 148 165\n",
      " 172 171 145 121 178 305 173 152 354 347 158 185 349 183 352 177 200 192\n",
      " 361 207 174 330 134 350 334 283 153 197 133 241 193 235 194 261 260 216\n",
      " 169 209 238 215 141 189 187 223 284 214 202 211 168 230 203 188 232 709\n",
      " 219 162 196 190 259 228 176 250 201 186 199 180 206 205 224 222 182 210\n",
      " 275 212 229 218 208 191 181 179 246 255 226 288 253 252 262 236 256 234\n",
      " 254 468 213 237 198 195 239 263 265 274 217 220 307 221 233 257 227 276\n",
      " 225 264 311 277 204 290 266 270 294 319 282 251 322 291 269 240 271 184\n",
      " 231 268 247 273 300 301 267 244 306 293 309 272 242 295 285 243 308 398\n",
      " 303 245 424 279 331 281 339 434 357 325 329 278 332 343 345 360 348 367\n",
      " 353 373 374 406 400 326 379 399 316 341 320 385 355 363 358 296 422 390\n",
      " 335 370 376 375 397 289 542 403 383 384 359 393 337 362 365 435 386 378\n",
      " 313 351 287 471 462 411 450 318 372 371 454 532 445 389 388 407 443 437\n",
      " 451 391 405 412 419 420 426 433 440 429 418 447 461 605 457 475 464 482\n",
      " 626 489 496 503 510 517 524 531 538 545 552 559 566 573 580 587 594 601\n",
      " 608 615 622 629 396 410 395 423 408 409 448 465 387 414 476 479 467 490\n",
      " 493 478 504 507 458 518 521 377 444 380 463] \n",
      "\n",
      "arrival_date_year has [2015 2016 2017] \n",
      "\n",
      "arrival_date_month has ['July' 'August' 'September' 'October' 'November' 'December' 'January'\n",
      " 'February' 'March' 'April' 'May' 'June'] \n",
      "\n",
      "arrival_date_week_number has [27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50\n",
      " 51 52 53  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21\n",
      " 22 23 24 25 26] \n",
      "\n",
      "arrival_date_day_of_month has [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31] \n",
      "\n",
      "stays_in_weekend_nights has [ 0  1  2  4  3  6 13  8  5  7 12  9 16 10 14] \n",
      "\n",
      "stays_in_week_nights has [ 0  1  2  3  4  5 11  8 10  6  7 15  9 12 33 20 14 16 21 13 30 19 24 40\n",
      " 22 25 17 32 26 18 34 35 41] \n",
      "\n",
      "adults has [ 2  1  3  4 40 26 50 27 55  0 20  6  5 10] \n",
      "\n",
      "children has [ 0.  1.  2. 10.  3.] \n",
      "\n",
      "babies has [ 0  1  2 10  9] \n",
      "\n",
      "meal has ['BB' 'FB' 'HB' 'SC' 'Undefined'] \n",
      "\n",
      "country has ['PRT' 'GBR' 'USA' 'ESP' 'IRL' 'FRA' 'ROU' 'NOR' 'OMN' 'ARG' 'POL' 'DEU'\n",
      " 'BEL' 'CHE' 'CN' 'GRC' 'ITA' 'NLD' 'DNK' 'RUS' 'SWE' 'AUS' 'EST' 'CZE'\n",
      " 'BRA' 'FIN' 'MOZ' 'BWA' 'LUX' 'SVN' 'ALB' 'IND' 'CHN' 'MEX' 'MAR' 'UKR'\n",
      " 'SMR' 'LVA' 'PRI' 'SRB' 'CHL' 'AUT' 'BLR' 'LTU' 'TUR' 'ZAF' 'AGO' 'ISR'\n",
      " 'CYM' 'ZMB' 'CPV' 'ZWE' 'DZA' 'KOR' 'CRI' 'HUN' 'ARE' 'TUN' 'JAM' 'HRV'\n",
      " 'HKG' 'IRN' 'GEO' 'AND' 'GIB' 'URY' 'JEY' 'CAF' 'CYP' 'COL' 'GGY' 'KWT'\n",
      " 'NGA' 'MDV' 'VEN' 'SVK' 'FJI' 'KAZ' 'PAK' 'IDN' 'LBN' 'PHL' 'SEN' 'SYC'\n",
      " 'AZE' 'BHR' 'NZL' 'THA' 'DOM' 'MKD' 'MYS' 'ARM' 'JPN' 'LKA' 'CUB' 'CMR'\n",
      " 'BIH' 'MUS' 'COM' 'SUR' 'UGA' 'BGR' 'CIV' 'JOR' 'SYR' 'SGP' 'BDI' 'SAU'\n",
      " 'VNM' 'PLW' 'QAT' 'EGY' 'PER' 'MLT' 'MWI' 'ECU' 'MDG' 'ISL' 'UZB' 'NPL'\n",
      " 'BHS' 'MAC' 'TGO' 'TWN' 'DJI' 'STP' 'KNA' 'ETH' 'IRQ' 'HND' 'RWA' 'KHM'\n",
      " 'MCO' 'BGD' 'IMN' 'TJK' 'NIC' 'BEN' 'VGB' 'TZA' 'GAB' 'GHA' 'TMP' 'GLP'\n",
      " 'KEN' 'LIE' 'GNB' 'MNE' 'UMI' 'MYT' 'FRO' 'MMR' 'PAN' 'BFA' 'LBY' 'MLI'\n",
      " 'NAM' 'BOL' 'PRY' 'BRB' 'ABW' 'AIA' 'SLV' 'DMA' 'PYF' 'GUY' 'LCA' 'ATA'\n",
      " 'GTM' 'ASM' 'MRT' 'NCL' 'KIR' 'SDN' 'ATF' 'SLE' 'LAO'] \n",
      "\n",
      "market_segment has ['Direct' 'Corporate' 'Online TA' 'Offline TA/TO' 'Complementary' 'Groups'\n",
      " 'Aviation'] \n",
      "\n",
      "distribution_channel has ['Direct' 'Corporate' 'TA/TO' 'Undefined' 'GDS'] \n",
      "\n",
      "is_repeated_guest has [0 1] \n",
      "\n",
      "previous_cancellations has [ 0  1  2  3 26 25 14  4 24 19  5 21  6 13 11] \n",
      "\n",
      "previous_bookings_not_canceled has [ 0  1  2  3  5  4  6  7  8  9 10 11 12 13 14 15 21 24 25 27 28 30 16 17\n",
      " 18 19 20 22 23 26 29 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
      " 72] \n",
      "\n",
      "reserved_room_type has ['C' 'A' 'D' 'E' 'G' 'F' 'H' 'L' 'B' 'P'] \n",
      "\n",
      "assigned_room_type has ['C' 'A' 'D' 'E' 'G' 'F' 'I' 'B' 'H' 'L' 'K' 'P'] \n",
      "\n",
      "booking_changes has [ 3  4  0  1  2  5 17  6  8  7 10 16  9 13 12 20 14 15 11 21 18] \n",
      "\n",
      "deposit_type has ['No Deposit' 'Refundable' 'Non Refund'] \n",
      "\n",
      "agent has [  0. 304. 240. 303.  15. 241.   8. 250. 115.   5. 175. 134. 156. 243.\n",
      " 242.   3. 105.  40. 147. 306. 184.  96.   2. 127.  95. 146.   9. 177.\n",
      "   6. 143. 244. 149. 167. 300. 171. 305.  67. 196. 152. 142. 261. 104.\n",
      "  36.  26.  29. 258. 110.  71. 181.  88. 251. 275.  69. 248. 208. 256.\n",
      " 314. 126. 281. 273. 253. 185. 330. 334. 328. 326. 321. 324. 313.  38.\n",
      " 155.  68. 335. 308. 332.  94. 348. 310. 339. 375.  66. 327. 387. 298.\n",
      "  91. 245. 385. 257. 393. 168. 405. 249. 315.  75. 128. 307.  11. 436.\n",
      "   1. 201. 183. 223. 368. 336. 291. 464. 411. 481.  10. 154. 468. 410.\n",
      " 390. 440. 495. 492. 493. 434.  57. 531. 420. 483. 526. 472. 429.  16.\n",
      "  34. 252. 270.  47. 114. 301. 193. 182. 135. 350. 195. 352. 355. 159.\n",
      " 139. 363. 384. 360. 331. 367.  64.  78. 406. 163. 414. 333. 427. 431.\n",
      " 430. 426. 438. 433. 418. 441. 282. 432.  72. 450. 180. 454. 455.  59.\n",
      " 451. 254. 358. 469. 165. 467. 510. 337. 476. 502. 527. 479. 508. 535.\n",
      " 302. 497. 187.  13.   7.  27.  14.  22.  17.  28.  42.  20.  19.  45.\n",
      "  37.  61.  39.  21.  24.  41.  50.  30.  54.  52.  12.  44.  31.  83.\n",
      "  32.  63.  60.  55.  56.  89.  87. 118.  86.  85. 210. 214. 129. 179.\n",
      " 138. 174. 170. 153.  93. 151. 119.  35. 173.  58.  53. 133.  79. 235.\n",
      " 192. 191. 236. 162. 215. 157. 287. 132. 234.  98.  77. 103. 107. 262.\n",
      " 220. 121. 205. 378.  23. 296. 290. 229.  33. 286. 276. 425. 484. 323.\n",
      " 403. 219. 394. 509. 111. 423.   4.  70.  82.  81.  74.  92.  99.  90.\n",
      " 112. 117. 106. 148. 158. 144. 211. 213. 216. 232. 150. 267. 227. 247.\n",
      " 278. 280. 285. 289. 269. 295. 265. 288. 122. 294. 325. 341. 344. 346.\n",
      " 359. 283. 364. 370. 371.  25. 141. 391. 397. 416. 404. 299. 197.  73.\n",
      " 354. 444. 408. 461. 388. 453. 459. 474. 475. 480. 449.] \n",
      "\n",
      "days_in_waiting_list has [  0  50  47  65 122  75 101 150 125  14  60  34 100  22 121  61  39   5\n",
      "   1   8 107  43  52   2  11 142 116  13  44  97  83   4 113  18  20 185\n",
      "  93 109   6  37 105 154  64  99  38  48  33  77  21  80  59  40  58  89\n",
      "  53  49  69  87  91  57 111  79  98  85  63  15   3  41 224  31  56 187\n",
      " 176  71  55  96 236 259 207 215 160 120  30  32  27  62  24 108 147 379\n",
      "  70  35 178 330 223 174 162 391  68 193  10  76  16  28   9 165  17  25\n",
      "  46   7  84 175 183  23 117  12  54  26  73  45  19  42  72  81  92  74\n",
      " 167  36] \n",
      "\n",
      "customer_type has ['Transient' 'Contract' 'Transient-Party' 'Group'] \n",
      "\n",
      "adr has [  0.    75.    98.   ... 266.75 209.25 157.71] \n",
      "\n",
      "required_car_parking_spaces has [0 1 2 8 3] \n",
      "\n",
      "total_of_special_requests has [0 1 3 2 4 5] \n",
      "\n",
      "reservation_status has ['Check-Out' 'Canceled' 'No-Show'] \n",
      "\n",
      "reservation_status_date has ['2015-07-01' '2015-07-02' '2015-07-03' '2015-05-06' '2015-04-22'\n",
      " '2015-06-23' '2015-07-05' '2015-07-06' '2015-07-07' '2015-07-08'\n",
      " '2015-05-11' '2015-07-16' '2015-05-29' '2015-05-19' '2015-06-19'\n",
      " '2015-05-23' '2015-05-18' '2015-07-09' '2015-06-02' '2015-07-13'\n",
      " '2015-07-04' '2015-06-29' '2015-06-16' '2015-06-18' '2015-06-12'\n",
      " '2015-06-09' '2015-05-26' '2015-07-11' '2015-07-12' '2015-07-17'\n",
      " '2015-04-15' '2015-05-13' '2015-07-10' '2015-05-20' '2015-05-12'\n",
      " '2015-07-14' '2015-06-17' '2015-05-01' '2015-03-30' '2015-07-15'\n",
      " '2015-07-19' '2015-06-03' '2015-06-26' '2015-05-14' '2015-07-20'\n",
      " '2015-05-07' '2015-05-28' '2015-04-13' '2015-03-25' '2015-07-21'\n",
      " '2015-06-27' '2015-07-18' '2015-07-23' '2015-06-08' '2015-06-22'\n",
      " '2015-06-24' '2015-03-05' '2015-06-01' '2015-04-24' '2015-07-22'\n",
      " '2015-05-27' '2015-04-06' '2015-04-11' '2015-07-25' '2015-07-28'\n",
      " '2015-07-29' '2015-06-25' '2015-07-24' '2015-06-05' '2015-06-30'\n",
      " '2015-06-13' '2015-06-11' '2015-07-30' '2015-07-27' '2015-04-29'\n",
      " '2015-06-04' '2015-07-26' '2015-08-01' '2015-08-02' '2015-06-15'\n",
      " '2015-04-23' '2015-07-31' '2015-05-25' '2015-08-03' '2015-04-17'\n",
      " '2015-08-04' '2015-08-06' '2015-05-15' '2015-05-09' '2015-03-17'\n",
      " '2015-05-22' '2015-08-07' '2015-04-04' '2015-08-05' '2015-08-08'\n",
      " '2015-08-10' '2015-05-04' '2015-06-06' '2015-08-09' '2015-08-15'\n",
      " '2015-08-11' '2015-03-28' '2015-08-14' '2015-08-12' '2015-08-16'\n",
      " '2015-05-16' '2015-08-21' '2015-08-13' '2015-08-17' '2015-04-20'\n",
      " '2015-08-18' '2015-08-23' '2015-08-22' '2015-08-19' '2015-08-20'\n",
      " '2015-08-29' '2015-03-31' '2015-05-30' '2015-08-25' '2015-04-14'\n",
      " '2015-08-24' '2015-03-24' '2015-05-21' '2015-08-28' '2015-08-26'\n",
      " '2015-08-27' '2015-08-30' '2015-08-31' '2015-09-06' '2015-09-03'\n",
      " '2015-09-04' '2015-09-02' '2015-09-01' '2015-09-05' '2015-06-20'\n",
      " '2015-09-07' '2015-09-10' '2015-09-11' '2015-09-08' '2015-09-09'\n",
      " '2015-09-13' '2015-09-15' '2015-04-10' '2015-01-02' '2014-11-18'\n",
      " '2015-09-12' '2015-09-17' '2015-09-14' '2015-04-07' '2015-09-19'\n",
      " '2015-09-16' '2015-09-20' '2015-01-18' '2015-10-23' '2015-01-22'\n",
      " '2015-01-01' '2015-09-22' '2015-09-24' '2015-09-18' '2015-09-21'\n",
      " '2015-09-30' '2015-09-25' '2015-09-27' '2015-09-28' '2015-10-12'\n",
      " '2015-09-29' '2015-09-23' '2015-10-01' '2015-09-26' '2015-04-18'\n",
      " '2015-10-02' '2015-10-04' '2015-10-08' '2015-10-03' '2015-10-07'\n",
      " '2015-10-09' '2015-10-11' '2015-10-05' '2015-10-06' '2015-10-10'\n",
      " '2015-10-14' '2015-10-15' '2015-10-18' '2015-10-13' '2015-10-20'\n",
      " '2015-10-19' '2015-10-31' '2015-10-16' '2015-10-21' '2015-10-22'\n",
      " '2015-10-17' '2015-10-24' '2015-10-25' '2015-10-28' '2015-10-27'\n",
      " '2015-10-26' '2015-10-30' '2015-11-05' '2015-10-29' '2015-11-03'\n",
      " '2015-11-07' '2015-11-04' '2015-11-01' '2015-11-02' '2015-11-17'\n",
      " '2015-11-06' '2015-11-10' '2015-11-08' '2015-11-09' '2015-11-15'\n",
      " '2015-11-16' '2015-11-11' '2015-11-12' '2015-11-14' '2015-11-13'\n",
      " '2015-11-18' '2015-11-22' '2015-11-19' '2015-11-21' '2015-11-20'\n",
      " '2015-11-24' '2015-11-25' '2015-11-23' '2015-11-28' '2015-11-26'\n",
      " '2015-11-27' '2015-11-29' '2015-12-04' '2015-12-01' '2015-12-06'\n",
      " '2015-12-08' '2015-12-02' '2015-12-03' '2015-12-31' '2015-12-05'\n",
      " '2015-12-10' '2015-12-17' '2015-11-30' '2015-12-12' '2015-12-07'\n",
      " '2016-01-05' '2015-12-11' '2015-12-13' '2015-12-15' '2015-12-16'\n",
      " '2015-12-19' '2015-12-18' '2015-12-26' '2015-12-27' '2015-12-22'\n",
      " '2015-12-23' '2015-12-24' '2015-12-29' '2015-12-28' '2015-12-20'\n",
      " '2015-12-30' '2016-01-02' '2016-01-01' '2015-12-25' '2016-01-03'\n",
      " '2016-01-04' '2016-01-11' '2016-01-07' '2015-12-21' '2016-01-09'\n",
      " '2016-01-10' '2016-01-08' '2016-01-06' '2016-01-12' '2016-01-13'\n",
      " '2016-01-23' '2016-02-09' '2016-01-15' '2016-01-16' '2016-01-17'\n",
      " '2016-01-19' '2016-01-18' '2016-01-21' '2016-01-24' '2016-01-22'\n",
      " '2016-01-29' '2016-01-27' '2016-01-25' '2016-03-08' '2016-01-26'\n",
      " '2016-01-20' '2016-01-30' '2016-02-01' '2016-02-02' '2016-02-08'\n",
      " '2016-02-07' '2016-01-28' '2016-02-05' '2016-02-03' '2016-02-13'\n",
      " '2016-02-10' '2016-02-04' '2016-02-12' '2016-02-11' '2016-02-16'\n",
      " '2016-02-14' '2016-02-15' '2016-02-20' '2016-02-06' '2016-01-14'\n",
      " '2016-02-17' '2016-02-21' '2016-02-24' '2016-02-25' '2016-02-19'\n",
      " '2016-02-18' '2016-02-26' '2016-02-23' '2016-03-05' '2016-02-22'\n",
      " '2016-02-27' '2016-03-03' '2016-03-24' '2016-03-04' '2016-02-29'\n",
      " '2016-03-01' '2016-03-02' '2016-03-30' '2016-03-07' '2016-03-14'\n",
      " '2016-03-21' '2016-03-09' '2016-03-12' '2016-03-22' '2016-03-10'\n",
      " '2016-03-11' '2016-03-20' '2016-03-15' '2016-03-17' '2016-03-16'\n",
      " '2016-03-19' '2016-03-27' '2016-03-18' '2016-03-26' '2016-03-31'\n",
      " '2016-03-28' '2016-03-29' '2016-04-01' '2016-03-23' '2016-04-02'\n",
      " '2016-03-25' '2016-03-13' '2016-04-04' '2016-04-03' '2016-04-05'\n",
      " '2016-04-08' '2016-04-06' '2016-04-09' '2016-04-12' '2016-04-16'\n",
      " '2016-04-17' '2016-04-27' '2016-04-14' '2016-04-18' '2016-04-21'\n",
      " '2016-04-19' '2016-04-20' '2016-04-10' '2016-04-13' '2016-04-11'\n",
      " '2016-04-07' '2016-04-15' '2016-04-22' '2016-04-23' '2016-04-26'\n",
      " '2016-04-28' '2016-04-24' '2016-04-25' '2016-04-29' '2016-04-30'\n",
      " '2016-05-01' '2016-05-10' '2016-05-02' '2016-05-07' '2016-05-08'\n",
      " '2016-05-12' '2016-05-04' '2016-05-06' '2016-05-03' '2016-05-09'\n",
      " '2016-05-05' '2016-05-13' '2016-05-14' '2016-05-18' '2016-05-19'\n",
      " '2016-05-15' '2016-05-16' '2016-05-11' '2016-05-21' '2016-05-22'\n",
      " '2016-05-20' '2016-05-24' '2016-05-25' '2016-05-26' '2016-05-23'\n",
      " '2016-05-27' '2016-05-17' '2016-05-29' '2016-05-28' '2016-05-30'\n",
      " '2016-05-31' '2016-06-01' '2016-06-03' '2016-06-08' '2016-06-02'\n",
      " '2016-06-05' '2016-06-06' '2016-06-13' '2016-06-07' '2016-06-10'\n",
      " '2016-06-11' '2016-06-16' '2016-06-12' '2016-06-14' '2016-06-17'\n",
      " '2016-06-04' '2016-06-18' '2016-06-21' '2016-06-09' '2016-06-24'\n",
      " '2016-06-20' '2016-06-25' '2016-06-22' '2016-06-26' '2016-06-23'\n",
      " '2016-07-01' '2016-06-15' '2016-06-28' '2016-07-02' '2016-06-19'\n",
      " '2016-06-27' '2016-07-04' '2016-06-30' '2016-07-05' '2016-07-08'\n",
      " '2016-07-09' '2016-07-07' '2016-07-12' '2016-06-29' '2016-07-10'\n",
      " '2016-07-15' '2016-07-03' '2016-07-16' '2016-07-14' '2016-07-18'\n",
      " '2016-07-13' '2016-07-06' '2016-07-20' '2016-07-21' '2016-07-23'\n",
      " '2016-07-19' '2016-07-11' '2016-07-28' '2016-07-17' '2016-07-25'\n",
      " '2016-07-22' '2016-07-29' '2016-08-03' '2016-08-02' '2016-08-04'\n",
      " '2016-08-08' '2016-08-10' '2016-08-01' '2016-08-06' '2016-03-06'\n",
      " '2016-08-05' '2016-07-26' '2016-08-07' '2016-07-30' '2016-07-24'\n",
      " '2016-08-12' '2016-07-27' '2016-08-13' '2016-08-18' '2016-08-16'\n",
      " '2016-08-15' '2016-08-17' '2016-08-11' '2016-07-31' '2016-08-19'\n",
      " '2016-09-01' '2016-08-23' '2016-08-26' '2016-08-20' '2016-08-21'\n",
      " '2016-09-04' '2016-08-22' '2016-08-27' '2016-08-25' '2016-08-09'\n",
      " '2016-09-05' '2016-08-24' '2016-09-10' '2016-08-29' '2016-09-09'\n",
      " '2016-08-30' '2016-09-13' '2016-08-31' '2016-09-14' '2016-09-12'\n",
      " '2016-09-15' '2016-08-14' '2016-09-02' '2016-09-08' '2016-09-19'\n",
      " '2016-09-16' '2016-09-07' '2016-09-21' '2016-09-06' '2016-09-22'\n",
      " '2016-09-17' '2016-09-20' '2016-09-03' '2016-09-26' '2016-09-23'\n",
      " '2016-09-18' '2016-09-29' '2016-10-02' '2016-10-01' '2016-09-27'\n",
      " '2016-09-25' '2016-10-05' '2016-09-11' '2016-09-30' '2016-10-09'\n",
      " '2016-10-03' '2016-10-06' '2016-10-11' '2016-09-24' '2016-10-13'\n",
      " '2016-09-28' '2016-10-08' '2016-10-07' '2016-10-16' '2016-08-28'\n",
      " '2016-10-17' '2016-10-18' '2016-10-10' '2016-10-04' '2016-10-15'\n",
      " '2016-10-19' '2016-10-21' '2016-10-12' '2016-10-24' '2016-10-26'\n",
      " '2016-10-23' '2016-10-20' '2016-10-25' '2016-10-27' '2016-10-28'\n",
      " '2016-10-30' '2016-10-29' '2016-11-01' '2016-11-04' '2016-10-14'\n",
      " '2016-11-07' '2016-11-03' '2016-11-10' '2016-11-14' '2016-11-02'\n",
      " '2016-10-31' '2016-11-11' '2016-11-08' '2016-11-05' '2016-11-25'\n",
      " '2016-11-09' '2016-11-20' '2016-10-22' '2016-11-22' '2016-11-16'\n",
      " '2016-11-23' '2016-11-17' '2016-11-06' '2016-11-15' '2016-11-13'\n",
      " '2016-11-12' '2016-11-21' '2016-11-27' '2016-11-19' '2016-11-30'\n",
      " '2016-11-18' '2016-12-02' '2016-12-04' '2016-11-29' '2016-12-07'\n",
      " '2016-11-28' '2016-12-03' '2016-12-06' '2016-11-24' '2016-12-08'\n",
      " '2016-12-05' '2016-12-10' '2016-12-13' '2016-12-14' '2016-12-16'\n",
      " '2016-12-15' '2016-12-17' '2016-12-19' '2016-12-21' '2016-12-20'\n",
      " '2016-12-22' '2016-12-23' '2016-12-24' '2016-12-01' '2016-12-27'\n",
      " '2016-12-29' '2016-12-30' '2016-12-12' '2017-01-02' '2016-12-11'\n",
      " '2017-01-03' '2017-01-04' '2017-01-01' '2016-12-26' '2017-01-06'\n",
      " '2016-12-28' '2016-12-18' '2017-01-10' '2017-01-11' '2017-01-07'\n",
      " '2017-01-12' '2017-01-16' '2017-01-14' '2017-01-13' '2017-01-05'\n",
      " '2017-01-17' '2017-01-20' '2016-12-09' '2017-01-26' '2016-12-31'\n",
      " '2017-01-23' '2017-01-27' '2017-01-28' '2017-01-19' '2017-01-25'\n",
      " '2017-01-24' '2017-01-29' '2017-01-18' '2016-12-25' '2017-01-15'\n",
      " '2017-01-21' '2017-02-01' '2017-02-02' '2017-01-31' '2017-02-03'\n",
      " '2017-02-04' '2017-02-06' '2017-02-07' '2017-02-08' '2017-01-30'\n",
      " '2017-02-09' '2017-01-09' '2017-02-11' '2017-02-10' '2017-02-12'\n",
      " '2017-02-13' '2017-02-14' '2017-02-16' '2017-02-17' '2017-02-18'\n",
      " '2017-02-19' '2017-02-20' '2017-02-15' '2017-02-21' '2017-02-22'\n",
      " '2017-02-26' '2017-02-23' '2017-02-24' '2017-02-25' '2017-02-28'\n",
      " '2017-03-05' '2017-02-27' '2017-03-03' '2017-03-06' '2017-03-02'\n",
      " '2017-03-08' '2017-03-09' '2017-03-10' '2017-03-07' '2017-03-12'\n",
      " '2017-03-13' '2017-03-14' '2017-03-01' '2017-03-18' '2017-03-17'\n",
      " '2017-03-24' '2017-03-22' '2017-03-26' '2017-03-27' '2017-03-11'\n",
      " '2017-03-28' '2017-03-29' '2017-03-30' '2017-03-31' '2017-03-19'\n",
      " '2017-01-22' '2017-04-02' '2017-03-20' '2017-04-03' '2017-01-08'\n",
      " '2017-03-23' '2017-04-05' '2017-02-05' '2017-04-04' '2017-03-15'\n",
      " '2017-04-07' '2017-03-25' '2017-04-08' '2017-04-06' '2017-03-21'\n",
      " '2017-04-10' '2017-04-01' '2017-04-11' '2017-04-13' '2017-04-15'\n",
      " '2017-04-12' '2017-03-04' '2017-04-19' '2017-04-22' '2017-04-20'\n",
      " '2017-05-02' '2017-04-09' '2017-04-23' '2017-04-24' '2017-04-16'\n",
      " '2017-04-28' '2017-04-18' '2017-04-26' '2017-04-25' '2017-04-17'\n",
      " '2017-04-21' '2017-05-03' '2017-05-04' '2017-03-16' '2017-05-05'\n",
      " '2017-04-29' '2017-04-14' '2017-05-08' '2017-04-27' '2017-05-11'\n",
      " '2017-05-01' '2017-05-10' '2017-05-13' '2017-05-06' '2017-05-14'\n",
      " '2017-05-16' '2017-04-30' '2017-05-15' '2017-05-07' '2017-05-09'\n",
      " '2017-05-17' '2017-05-21' '2017-05-12' '2017-05-22' '2017-05-24'\n",
      " '2017-05-23' '2017-05-25' '2017-05-26' '2017-05-28' '2017-05-27'\n",
      " '2017-05-29' '2017-05-19' '2017-05-31' '2017-05-20' '2017-06-01'\n",
      " '2017-05-30' '2017-06-02' '2016-11-26' '2017-06-04' '2017-06-05'\n",
      " '2017-06-06' '2017-06-07' '2017-05-18' '2017-06-09' '2017-06-10'\n",
      " '2017-06-11' '2017-06-12' '2017-06-14' '2017-06-08' '2017-06-16'\n",
      " '2017-06-13' '2017-06-03' '2017-06-24' '2017-06-20' '2017-06-19'\n",
      " '2017-06-21' '2017-06-26' '2017-06-27' '2017-06-22' '2017-06-28'\n",
      " '2017-06-15' '2017-06-29' '2017-06-30' '2017-06-18' '2017-07-04'\n",
      " '2017-07-08' '2017-07-05' '2017-07-03' '2017-07-07' '2017-07-01'\n",
      " '2017-07-06' '2017-07-11' '2017-07-12' '2017-06-23' '2017-07-13'\n",
      " '2017-07-02' '2017-07-10' '2017-07-14' '2017-07-15' '2017-07-16'\n",
      " '2017-07-18' '2017-07-17' '2017-07-19' '2017-07-20' '2017-07-21'\n",
      " '2017-06-25' '2017-06-17' '2017-07-24' '2017-07-26' '2017-07-09'\n",
      " '2017-07-27' '2017-07-28' '2017-07-31' '2017-07-29' '2017-07-22'\n",
      " '2017-08-02' '2017-08-01' '2017-08-03' '2017-08-04' '2017-07-25'\n",
      " '2017-07-23' '2017-08-09' '2017-08-10' '2017-07-30' '2017-08-07'\n",
      " '2017-08-13' '2017-08-05' '2017-08-14' '2017-08-08' '2017-08-16'\n",
      " '2017-08-17' '2017-08-15' '2017-08-18' '2017-08-20' '2017-08-22'\n",
      " '2017-08-06' '2017-08-25' '2017-08-26' '2017-08-23' '2017-08-11'\n",
      " '2017-08-27' '2017-08-21' '2017-08-29' '2017-08-31' '2017-08-12'\n",
      " '2017-08-19' '2017-09-01' '2017-08-28' '2015-04-03' '2015-01-21'\n",
      " '2015-01-28' '2015-01-29' '2015-01-30' '2015-02-02' '2015-02-05'\n",
      " '2015-02-06' '2015-02-09' '2015-02-10' '2015-02-11' '2015-02-12'\n",
      " '2015-02-19' '2015-02-20' '2015-02-23' '2015-02-24' '2015-02-25'\n",
      " '2015-02-26' '2015-02-27' '2015-03-03' '2015-03-04' '2015-03-06'\n",
      " '2015-03-09' '2015-03-11' '2015-03-12' '2015-03-18' '2015-04-02'\n",
      " '2015-06-14' '2015-04-08' '2015-04-16' '2015-04-25' '2015-04-28'\n",
      " '2015-05-08' '2016-01-31' '2017-09-06' '2016-02-28' '2015-12-09'\n",
      " '2015-12-14' '2017-09-09' '2017-09-02' '2017-08-24' '2017-08-30'\n",
      " '2017-09-03' '2017-09-04' '2017-09-05' '2017-09-07' '2017-09-08'\n",
      " '2017-09-10' '2017-09-12' '2017-09-14' '2015-04-30' '2015-04-21'\n",
      " '2015-04-05' '2015-03-13' '2015-05-05' '2015-03-29' '2015-06-10'\n",
      " '2015-04-27' '2014-10-17' '2015-01-20' '2015-02-17' '2015-03-10'\n",
      " '2015-03-23'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i ,'has', df[i].unique(),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffa6053c",
   "metadata": {},
   "source": [
    "next step is to convert datatype of children from float to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7884bec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2, 10,  3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.children= df.children.astype(int)\n",
    "df.children.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a6ae8c",
   "metadata": {},
   "source": [
    "now for agent datatype, we need to convert in from float to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df20b014",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove decimal\n",
    "df.agent=df.agent.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0882d970",
   "metadata": {},
   "source": [
    "logically speaking, hotel usually cant be booked for children and babies. so we can drop that rows. Adults has value 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56c8fae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['adults']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8646580",
   "metadata": {},
   "source": [
    "Now, we need to create 2 DataFrames, one for booking which are cancelled while the other are for when it's not cancelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e6c701e",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'January':1, 'February':2, 'March':3, 'April':4,'May':5,'June':6,'July':7,\n",
    "    'August':8,'September':9,'October':10,'November':11,'December':12}\n",
    "df.arrival_date_month = df.arrival_date_month.map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6b8af0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_not_c =df[df['is_canceled']==0] #dataframe for bookings not cancelled\n",
    "df_c = df[df['is_canceled']==1] #dataframe for bookings cancelled\n",
    "df_not_c=df_not_c.drop('is_canceled',axis=1)\n",
    "df_c=df_c.drop('is_canceled',axis=1,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec889bd",
   "metadata": {},
   "source": [
    "# Data Analysis (Q1-Q3 : Descriptive Analytics & Insight)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec67795e",
   "metadata": {},
   "source": [
    "hotel vs number of guest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c377bd1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hotel v No. of Guest')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAE/CAYAAAAHXnZuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdEUlEQVR4nO3de9RddX3n8ffHxCIdFbkEhASJA9SKWGmJFKuOl9iC9QIzhTG2ldBmmimDvTjjtGC7vHXSAdvRjrXoUFECUiGlItGlFVaoWlsKBsvFQClRVGIoBLnqEiTpd/44v8ceHp48Obmc35OE92uts87e371/e//2k7UOH37nt/dJVSFJkiSpjyfNdAckSZKkJxIDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlaReQ5J1JPjbT/dgeGfhokvuSXDvT/ZGkmWIAl6RtkOQbSV41qXZqki+N2P78JP9rPL2b9rwvT1JJ/mxS/UtJTh3z6V8C/Cwwr6qO2Uz/Dkzy50nWJ/lukq+3v9WPj7NjST6f5L+M8xySNMEALklPPN8DTkkyv/N5DwG+UVXfm2pjkn2Bvwd+FHgp8DTgp4AvMAjukrRbMIBL0pgkeW4bWb0/yZokr2/1pcAvAb/TRnk/1eoHJfmrJBuS3J7kN0c8zy1JXju0PjvJPUl+ajNN7gfOB96xmeM9KcnvJ/lmkruTXJBkrxH7clCSlUnuTbI2ya+1+hLgw8CL2jW/a4rmbwEeBN5UVV+rgfur6qNV9aftOC9Psm7SOX/4bUTr+xlJvpbkO0lWJNmnbXtKko+1+v1JvpzkgCTLGAT+D7S+fWCUa5WkbWUAl6QxSPJk4FPAFcD+wG8AFyV5TlWdC1wEvKeqnlpVr0vypLb/DcBcYCHw20mOG+F0HwfeOLR+HHBPVX1lmjbLgF9I8pwptp3aXq8A/j3wVGDUUPpxYB1wEHAS8IdJFlbVecCvA1e3a54q/L8KuKyq/nXEc03lN4ETgZe1PtwHTEy3WQzsBRwM7Nv68/2q+j3gb4E3t769eTvOL0lbZACXpG33yTaSen+S+4FzhrYdyyC4nlVVP6iqq4BP89igPOyFwJyqenfb/+vAnwOLRujHXwCvT/Kjbf0XW22zqupfgA8B755i8y8B762qr1fVd4EzgUVJZk93zCQHM5jn/btV9XBVXc9g1PtNI1wDwH7Avwwd7/Xtb/tQkitGPMZ/BX6vqtZV1SPAO4GTWt8fZRC8D6uqTVV1XVU9OOJxJWmHMYBL0rY7saqeMfEC/tvQtoOAOyaN5n6Twej2VA4BDpoU6N8GHLClTlTVWuAW4HUthL+eLQTw5mzguCQvmFQ/qPV1uN+zR+jLQcC9VfXQpLabu+bJvgMcOLFSVSvb3/UtwI+MeIxDgMuG/oa3AJsY9P1C4HPAxe0mz/e0byokqSsDuCSNx3rg4Da1ZMKzgG+35Zq0/x3A7cOBvqqeVlU/P+L5JqahnADc3EL5tKrqO8CfAH8wRd8PmdTvjcBdWzjkemCfJE+b1Pbbm9l/slXAiZP+ZpN9j8FNmgAkmQXMGdp+B/DqSX/Hp1TVt6vq0ap6V1UdAfwM8FrglNZu8r+HJI2NAVySxuMaBmHxd5I8OcnLgdcBF7ftdzGYXz3hWuDBJL+bZM8ks5IcmeSFI57vYuDngNMYbfR7wnsZhNHnDtU+DrwlybOTPBX4Q+CSqto43YGq6g4GTzH53+2Gx58AljCY7z5qX/YGLkxyaHtu+NOAo4b2+WfgKUle00avfx/YY2j7h4BlSQ4BSDInyQlt+RVJnt9C+4MMpqRsau0m/3tI0tgYwCVpDKrqBwymgrwauIfB/PBTquqf2i7nAUe0qRKfrKpNDAL6UcDtrc2HGdw0OMr57gSuZhCmL9mKfj4IvAfYZ6j8EQbTNb7Y+vIwg5tISfLSJN+d5pBvBOYzGA2/DHhHVV05Yl/uYTB3/mHgS8BDwPUMHkd4WtvnAQZTfT7MYGT9ewxu+pzwf4GVwBVJHgL+Afjptu2ZwKUMwvctDB5v+LGhdidl8CNB7x+lv5K0rVLlt26SJElSL46AS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktTRtD8rvDvab7/9av78+TPdDUmSJO3mrrvuunuqas7k+hMugM+fP5/Vq1fPdDckSZK0m0vyzanqTkGRJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjmbPdAeeqI7+nxfMdBck7QKu+6NTZroLkqQdzBFwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLU0VgDeJJvJLkpyfVJVrfaPkmuTHJbe997aP8zk6xNcmuS44bqR7fjrE3y/iRp9T2SXNLq1ySZP87rkSRJkrZXjxHwV1TVUVW1oK2fAayqqsOBVW2dJEcAi4DnAccD5ySZ1dp8EFgKHN5ex7f6EuC+qjoMeB9wdofrkSRJkrbZTExBOQFY3paXAycO1S+uqkeq6nZgLXBMkgOBp1fV1VVVwAWT2kwc61Jg4cTouCRJkrQzGncAL+CKJNclWdpqB1TVnQDtff9WnwvcMdR2XavNbcuT649pU1UbgQeAfcdwHZIkSdIOMXvMx39xVa1Psj9wZZJ/mmbfqUaua5r6dG0ee+BB+F8K8KxnPWv6HkuSJEljNNYR8Kpa397vBi4DjgHuatNKaO93t93XAQcPNZ8HrG/1eVPUH9MmyWxgL+DeKfpxblUtqKoFc+bM2TEXJ0mSJG2DsQXwJP8uydMmloGfA74KrAQWt90WA5e35ZXAovZkk2czuNny2jZN5aEkx7b53adMajNxrJOAq9o8cUmSJGmnNM4pKAcAl7V7ImcDf1FVf53ky8CKJEuAbwEnA1TVmiQrgJuBjcDpVbWpHes04HxgT+Cz7QVwHnBhkrUMRr4XjfF6JEmSpO02tgBeVV8HXjBF/TvAws20WQYsm6K+GjhyivrDtAAvSZIk7Qr8JUxJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkdjD+BJZiX5xySfbuv7JLkyyW3tfe+hfc9MsjbJrUmOG6ofneSmtu39SdLqeyS5pNWvSTJ/3NcjSZIkbY8eI+C/BdwytH4GsKqqDgdWtXWSHAEsAp4HHA+ck2RWa/NBYClweHsd3+pLgPuq6jDgfcDZ470USZIkafuMNYAnmQe8BvjwUPkEYHlbXg6cOFS/uKoeqarbgbXAMUkOBJ5eVVdXVQEXTGozcaxLgYUTo+OSJEnSzmjcI+B/AvwO8K9DtQOq6k6A9r5/q88F7hjab12rzW3Lk+uPaVNVG4EHgH136BVIkiRJO9DYAniS1wJ3V9V1ozaZolbT1KdrM7kvS5OsTrJ6w4YNI3ZHkiRJ2vHGOQL+YuD1Sb4BXAy8MsnHgLvatBLa+91t/3XAwUPt5wHrW33eFPXHtEkyG9gLuHdyR6rq3KpaUFUL5syZs2OuTpIkSdoGYwvgVXVmVc2rqvkMbq68qqp+GVgJLG67LQYub8srgUXtySbPZnCz5bVtmspDSY5t87tPmdRm4lgntXM8bgRckiRJ2lnMnoFzngWsSLIE+BZwMkBVrUmyArgZ2AicXlWbWpvTgPOBPYHPthfAecCFSdYyGPle1OsiJEmSpG3RJYBX1eeBz7fl7wALN7PfMmDZFPXVwJFT1B+mBXhJkiRpV+AvYUqSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjmbPdAckSRrFt979/JnugqRdxLPeftNMd2FajoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktTR2AJ4kqckuTbJDUnWJHlXq++T5Mokt7X3vYfanJlkbZJbkxw3VD86yU1t2/uTpNX3SHJJq1+TZP64rkeSJEnaEcY5Av4I8MqqegFwFHB8kmOBM4BVVXU4sKqtk+QIYBHwPOB44Jwks9qxPggsBQ5vr+NbfQlwX1UdBrwPOHuM1yNJkiRtt7EF8Br4blt9cnsVcAKwvNWXAye25ROAi6vqkaq6HVgLHJPkQODpVXV1VRVwwaQ2E8e6FFg4MTouSZIk7YzGOgc8yawk1wN3A1dW1TXAAVV1J0B737/tPhe4Y6j5ulab25Yn1x/Tpqo2Ag8A+07Rj6VJVidZvWHDhh10dZIkSdLWmz3dxiT/abrtVfWJLWzfBByV5BnAZUmOnO50Ux1imvp0bSb341zgXIAFCxY8brskSZLUy7QBHHjdNNsKmDaA/3DHqvuTfJ7B3O27khxYVXe26SV3t93WAQcPNZsHrG/1eVPUh9usSzIb2Au4d5Q+SZIkSTNh2gBeVb+yrQdOMgd4tIXvPYFXMbhJciWwGDirvV/emqwE/iLJe4GDGNxseW1VbUryULuB8xrgFOBPh9osBq4GTgKuavPEJUmSpJ3SlkbAAUhyAPCHwEFV9er2xJIXVdV50zQ7EFjenmTyJGBFVX06ydXAiiRLgG8BJwNU1ZokK4CbgY3A6W0KC8BpwPnAnsBn2wvgPODCJGsZjHwvGvG6JUmSpBkxUgBnEH4/CvxeW/9n4BIGAXhKVXUj8JNT1L8DLNxMm2XAsinqq4HHzR+vqodpAV6SJEnaFYz6FJT9qmoF8K/wwyeObJq+iSRJkqTJRg3g30uyL+0JI20+9gNj65UkSZK0mxp1Csp/Z3DD46FJ/g6Yg1M/JEmSpK02agBfA7wMeA6DZ2/fyph/xEeSJEnaHY0aoq+uqo1VtaaqvlpVjzJ49J8kSZKkrbClX8J8JoOfe98zyU/yb788+XTgR8fcN0mSJGm3s6UpKMcBpzL49cn3DtUfAt42pj5JkiRJu60t/RLmcgY/pvMLVfVXnfokSZIk7bZGnQO+Ksl7k6xur/+TZK+x9kySJEnaDY0awM9jMO3kP7fXgwx+GVOSJEnSVhj1MYSHVtUvDK2/K8n1Y+iPJEmStFsbdQT8+0leMrGS5MXA98fTJUmSJGn3NeoI+GkMbsacmPd9H7B4PF2SJEmSdl+jBvBbgPcAhwLPAB4ATgRuHEuvJEmSpN3UqAH8cuB+4CvAt8fWG0mSJGk3N2oAn1dVx4+1J5IkSdITwKg3Yf59kuePtSeSJEnSE8C0I+BJbgKq7fcrSb4OPAIEqKr6ifF3UZIkSdp9bGkKymu79EKSJEl6gpg2gFfVN3t1RJIkSXoiGHUOuCRJkqQdwAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOhpbAE9ycJK/SXJLkjVJfqvV90lyZZLb2vveQ23OTLI2ya1JjhuqH53kprbt/UnS6nskuaTVr0kyf1zXI0mSJO0I4xwB3wj8j6p6LnAscHqSI4AzgFVVdTiwqq3Tti0CngccD5yTZFY71geBpcDh7XV8qy8B7quqw4D3AWeP8XokSZKk7Ta2AF5Vd1bVV9ryQ8AtwFzgBGB52205cGJbPgG4uKoeqarbgbXAMUkOBJ5eVVdXVQEXTGozcaxLgYUTo+OSJEnSzqjLHPA2NeQngWuAA6rqThiEdGD/tttc4I6hZutabW5bnlx/TJuq2gg8AOw7louQJEmSdoCxB/AkTwX+Cvjtqnpwul2nqNU09enaTO7D0iSrk6zesGHDlrosSZIkjc1YA3iSJzMI3xdV1Sda+a42rYT2fnerrwMOHmo+D1jf6vOmqD+mTZLZwF7AvZP7UVXnVtWCqlowZ86cHXFpkiRJ0jYZ51NQApwH3FJV7x3atBJY3JYXA5cP1Re1J5s8m8HNlte2aSoPJTm2HfOUSW0mjnUScFWbJy5JkiTtlGaP8dgvBt4E3JTk+lZ7G3AWsCLJEuBbwMkAVbUmyQrgZgZPUDm9qja1dqcB5wN7Ap9tLxgE/AuTrGUw8r1ojNcjSZIkbbexBfCq+hJTz9EGWLiZNsuAZVPUVwNHTlF/mBbgJUmSpF2Bv4QpSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSepobAE8yUeS3J3kq0O1fZJcmeS29r730LYzk6xNcmuS44bqRye5qW17f5K0+h5JLmn1a5LMH9e1SJIkSTvKOEfAzweOn1Q7A1hVVYcDq9o6SY4AFgHPa23OSTKrtfkgsBQ4vL0mjrkEuK+qDgPeB5w9tiuRJEmSdpCxBfCq+iJw76TyCcDytrwcOHGofnFVPVJVtwNrgWOSHAg8vaqurqoCLpjUZuJYlwILJ0bHJUmSpJ1V7zngB1TVnQDtff9WnwvcMbTfulab25Yn1x/Tpqo2Ag8A+46t55IkSdIOsLPchDnVyHVNU5+uzeMPnixNsjrJ6g0bNmxjFyVJkqTt1zuA39WmldDe7271dcDBQ/vNA9a3+rwp6o9pk2Q2sBePn/ICQFWdW1ULqmrBnDlzdtClSJIkSVuvdwBfCSxuy4uBy4fqi9qTTZ7N4GbLa9s0lYeSHNvmd58yqc3EsU4CrmrzxCVJkqSd1uxxHTjJx4GXA/slWQe8AzgLWJFkCfAt4GSAqlqTZAVwM7AROL2qNrVDncbgiSp7Ap9tL4DzgAuTrGUw8r1oXNciSZIk7ShjC+BV9cbNbFq4mf2XAcumqK8Gjpyi/jAtwEuSJEm7ip3lJkxJkiTpCcEALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktSRAVySJEnqyAAuSZIkdWQAlyRJkjoygEuSJEkdGcAlSZKkjgzgkiRJUkcGcEmSJKkjA7gkSZLUkQFckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJHBnBJkiSpIwO4JEmS1JEBXJIkSerIAC5JkiR1ZACXJEmSOjKAS5IkSR0ZwCVJkqSODOCSJElSRwZwSZIkqSMDuCRJktTRLh/Akxyf5NYka5OcMdP9kSRJkqazSwfwJLOAPwNeDRwBvDHJETPbK0mSJGnzdukADhwDrK2qr1fVD4CLgRNmuE+SJEnSZu3qAXwucMfQ+rpWkyRJknZKs2e6A9spU9TqcTslS4GlbfW7SW4da6+kbbMfcM9Md0I7l/zx4pnugrSz87NTj/eOqSLijDhkquKuHsDXAQcPrc8D1k/eqarOBc7t1SlpWyRZXVULZrofkrQr8bNTu6JdfQrKl4HDkzw7yY8Ai4CVM9wnSZIkabN26RHwqtqY5M3A54BZwEeqas0Md0uSJEnarF06gANU1WeAz8x0P6QdwGlSkrT1/OzULidVj7tnUZIkSdKY7OpzwCVJkqRdigFc2gpJnpnk4iRfS3Jzks8k+bEkByW5tO1zVJKf38rjnprkA5Nqn08y7Z39Sd424vG/kWS/remTJG2tJJuSXJ/kq0k+leQZYzzX/CS/OM22r06qvTPJW7dwzFOTHDTCuc9PctLW9Vj6NwZwaURJAlwGfL6qDq2qI4C3AQdU1fqqmvgwPgrYqgC+HUYK4JLUyfer6qiqOhK4Fzh9HCdJMhuYD0wZwLfDqcAWA7i0vQzg0uheATxaVR+aKFTV9VX1txOjLe1xmO8G3tBGgd6Q5LYkcwCSPCnJ2q0djU7yxiQ3tXOc3WpnAXu281zUar+c5NpW+39JZu2oi5ekrXQ17depkxya5K+TXJfkb5P8eKuf3D7XbkjyxVZ7SpKPts+8f0zyilY/NclfJvkUcAVwFvDS9nn3lq3pWPum8h+S3JjksiR7txHtBcBF7Zh7Jjk6yRdavz+X5MAd9+fRE9ku/xQUqaMjgeum26GqfpDk7cCCqnozQPsPzS8BfwK8Crihqqb61bY3JHnJ0Pphrf1BwNnA0cB9wBVJTqyqM5K8uaqOavs9F3gD8OKqejTJOe28F2zrBUvStmj/878QOK+VzgV+vapuS/LTwDnAK4G3A8dV1beHpqucDlBVz2+fn1ck+bG27UXAT1TVvUleDry1ql67mW4cmuT6ofVnAn/cli8AfqOqvpDk3cA7quq326ON31pVq5M8GfhT4ISq2pDkDcAy4Fe39e8iTTCAS+P3EeByBgH8V4GPbma/SyZCOwzmgLfFFzKY9rKh1S8C/gPwyUntFzII6V8ezJZhT+DuHXEBkjSiPVvonc9gwOLKJE8Ffgb4y/bZBLBHe/874PwkK4BPtNpLGARfquqfknwTmAjgV1bVvSP25WsTAxQwmAPe3vcCnlFVX2iblgN/OUX75zAYeLmy9XsWcOeI55amZQCXRrcG2OqbbqrqjiR3JXkl8NMMRqW3Rra8yw/3W15VZ27l8SVpR/l+VR3VQu6nGYxmnw/cPxyGJ1TVr7cR8dcA1yc5iuk/8763w3u8eQHWVNWLOp5TTxDOAZdGdxWwR5JfmygkeWGSl03a7yHgaZNqHwY+Bqyoqk1bed5rgJcl2a99rftGYGLk5tH2NSnAKuCkJPu3vu2T5JCtPJckbbeqegD4TeCtwPeB25OcDIMb2pO8oC0fWlXXVNXbgXuAg4Ev0gYq2tSTZwG3TnGaqT5rR+3bfUle2kpv4t8+U4ePeSswJ8mLWl+enOR5W3s+aSoGcGlENfjVqv8I/GwGjyFcA7wTWD9p178Bjpi4CbPVVgJPZfPTT6Y7753Ame24NwBfqarL2+ZzgRuTXFRVNwO/z2C+5I3AlYA3DEmaEVX1jww+sxYxCNRLktzA4NvEE9pufzRxgzmD4H0Dg/nhs5LcBFwCnFpVj0xxihuBje0Gzq26CRNY3M59I4MnV7271c8HPtSm0cxi8K3n2a3f1zOYSiNtN38JU+ogg+d5v6+qXrrFnSVJ0m7NOeDSmCU5AziNrZ/7LUmSdkOOgEuSJEkdOQdckiRJ6sgALkmSJHVkAJckSZI6MoBLkiRJHRnAJUmSpI4M4JIkSVJH/x9B80xGAeGicgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x=df.hotel.value_counts().index,y=df.hotel.value_counts())\n",
    "plt.title('Hotel v No. of Guest')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a67e77e",
   "metadata": {},
   "source": [
    "Meal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1477ee10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "label=%{label}<br>value=%{value}<extra></extra>",
         "labels": [
          "BB",
          "SC",
          "HB",
          "Undefined",
          "FB"
         ],
         "legendgroup": "",
         "name": "",
         "showlegend": true,
         "type": "pie",
         "values": [
          67278,
          9387,
          9047,
          488,
          360
         ]
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.pie(data_frame=df.distribution_channel.value_counts(),names=df.distribution_channel.value_counts().index,values=df.distribution_channel.value_counts(),title='Distribution Channel')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83b02b1",
   "metadata": {},
   "source": [
    "Repeated Guest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f908d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of repeated guests =4.945344291071571%\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of repeated guests ={}%'.format(len(df_not_c[df_not_c['is_repeated_guest']==1])*100/len(df_not_c)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cd524e",
   "metadata": {},
   "source": [
    "Room types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4cbef246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Reserved Room Type')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x=df_not_c.reserved_room_type.value_counts().index,y=df_not_c.reserved_room_type.value_counts())\n",
    "plt.title('Reserved Room Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "474f9f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Assigned Room Type')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x=df_not_c.assigned_room_type.value_counts().index,y=df_not_c.assigned_room_type.value_counts())\n",
    "plt.title('Assigned Room Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd34766f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of bookings where assigned room type is not what was reserved room type= 19.364876725444827 %\n"
     ]
    }
   ],
   "source": [
    "print('Proportion of bookings where assigned room type is not what was reserved room type=', len(df_not_c[df_not_c['reserved_room_type']!=df_not_c['assigned_room_type']])*100/len(df_not_c),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8d7de9",
   "metadata": {},
   "source": [
    "Deposit Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f1393a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Deposit    62529\n",
       "Refundable       81\n",
       "Non Refund       55\n",
       "Name: deposit_type, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_c.deposit_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7926f7b0",
   "metadata": {},
   "source": [
    "Customer Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0aa2446e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "label=%{label}<br>value=%{value}<extra></extra>",
         "labels": [
          "Transient",
          "Transient-Party",
          "Contract",
          "Group"
         ],
         "legendgroup": "",
         "name": "",
         "showlegend": true,
         "type": "pie",
         "values": [
          71317,
          11575,
          3134,
          534
         ]
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
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             ],
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             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
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              0.1111111111111111,
              "#46039f"
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             [
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              "#9c179e"
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             [
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              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
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             [
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             [
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              "#fdca26"
             ],
             [
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            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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   "metadata": {},
   "source": [
    "Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f71e1394",
   "metadata": {},
   "outputs": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>No.of Guests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRT</td>\n",
       "      <td>17542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBR</td>\n",
       "      <td>8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>7071</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESP</td>\n",
       "      <td>5372</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEU</td>\n",
       "      <td>4322</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>SYC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>MDG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>SMR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>FRO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
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      "text/plain": [
       "    Country  No.of Guests\n",
       "0       PRT         17542\n",
       "1       GBR          8428\n",
       "2       FRA          7071\n",
       "3       ESP          5372\n",
       "4       DEU          4322\n",
       "..      ...           ...\n",
       "160     ZMB             1\n",
       "161     SYC             1\n",
       "162     MDG             1\n",
       "163     SMR             1\n",
       "164     FRO             1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country = df_not_c['country'].value_counts().reset_index()\n",
    "df_country.columns = ['Country','No.of Guests']\n",
    "df_country"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6a94444a",
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    "px.choropleth(df_country,locations=df_country['Country'],color=df_country['No.of Guests'],\n",
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   "source": [
    "Hotel prices by months"
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   "execution_count": 34,
   "id": "b6e78f44",
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   "source": [
    "df_resort = df_not_c[(df_not_c['hotel'] == 'Resort Hotel')]\n",
    "df_city = df_not_c[(df_not_c['hotel'] == 'City Hotel')]"
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   "id": "a94cab4d",
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       "      <th>resort price</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>48.852694</td>\n",
       "      <td>83.729687</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53.977059</td>\n",
       "      <td>88.324398</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>91.334430</td>\n",
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       "      <td>6</td>\n",
       "      <td>109.200465</td>\n",
       "      <td>120.697766</td>\n",
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       "      <td>7</td>\n",
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       "      <td>120.346486</td>\n",
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       "      <td>10</td>\n",
       "      <td>62.830638</td>\n",
       "      <td>105.594411</td>\n",
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       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>49.162655</td>\n",
       "      <td>88.276587</td>\n",
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
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       "      <td>90.924499</td>\n",
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       "    month  resort price  city price\n",
       "0       1     48.852694   83.729687\n",
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       "2       3     57.512456   91.334430\n",
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       "5       6    109.200465  120.697766\n",
       "6       7    151.297719  120.346486\n",
       "7       8    182.256818  123.023174\n",
       "8       9     98.744746  116.957015\n",
       "9      10     62.830638  105.594411\n",
       "10     11     49.162655   88.276587\n",
       "11     12     64.223176   90.924499"
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    "resort = df_resort.groupby(['arrival_date_month'])['adr'].mean().reset_index()\n",
    "city=df_city.groupby(['arrival_date_month'])['adr'].mean().reset_index()\n",
    "\n",
    "combined=resort.merge(city, on = 'arrival_date_month')\n",
    "combined.columns = ['month', 'resort price', 'city price']\n",
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price per night over the Months\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.line(combined, x = 'month', y = ['resort price','city price'],\n",
    "        title = 'Room price per night over the Months')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dac4278",
   "metadata": {},
   "source": [
    "Busy Months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "62f269bf",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>no of guests</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2986</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2163</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2138</td>\n",
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       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>2137</td>\n",
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       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
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       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>1915</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1850</td>\n",
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       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>1706</td>\n",
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       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>1672</td>\n",
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       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1633</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "    month  no of guests\n",
       "0       8          3133\n",
       "1       7          2986\n",
       "2       5          2234\n",
       "3      10          2163\n",
       "4       3          2138\n",
       "5       4          2137\n",
       "6       2          1999\n",
       "7       6          1915\n",
       "8       9          1850\n",
       "9      11          1706\n",
       "10     12          1672\n",
       "11      1          1633"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resort_m = df_resort['arrival_date_month'].value_counts().reset_index()\n",
    "resort_m.columns=['month','no of guests']\n",
    "resort_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b5194419",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>3811</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3648</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>3469</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>3312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>3162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>2174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  no of guests\n",
       "0       8          4446\n",
       "1       7          3811\n",
       "2       5          3648\n",
       "3       6          3469\n",
       "4       3          3463\n",
       "5       4          3312\n",
       "6       9          3162\n",
       "7      10          3056\n",
       "8       2          2606\n",
       "9      11          2174\n",
       "10     12          2005\n",
       "11      1          1947"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_m = df_city['arrival_date_month'].value_counts().reset_index()\n",
    "city_m.columns=['month','no of guests']\n",
    "city_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9ef8af01",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4446</td>\n",
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       "      <td>7</td>\n",
       "      <td>2986</td>\n",
       "      <td>3811</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2234</td>\n",
       "      <td>3648</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2163</td>\n",
       "      <td>3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2138</td>\n",
       "      <td>3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>2137</td>\n",
       "      <td>3312</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1999</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>1915</td>\n",
       "      <td>3469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1850</td>\n",
       "      <td>3162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>1706</td>\n",
       "      <td>2174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>1672</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1633</td>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  no of guests in resort  no of guest in city hotel\n",
       "0       8                    3133                       4446\n",
       "1       7                    2986                       3811\n",
       "2       5                    2234                       3648\n",
       "3      10                    2163                       3056\n",
       "4       3                    2138                       3463\n",
       "5       4                    2137                       3312\n",
       "6       2                    1999                       2606\n",
       "7       6                    1915                       3469\n",
       "8       9                    1850                       3162\n",
       "9      11                    1706                       2174\n",
       "10     12                    1672                       2005\n",
       "11      1                    1633                       1947"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_m = resort_m.merge(city_m,on='month')\n",
    "combined_m.columns=['month','no of guests in resort','no of guest in city hotel']\n",
    "combined_m"
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  },
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "px.line(combined_m, x = 'month', y = ['no of guests in resort','no of guest in city hotel'],\n",
    "        title='Total no of guests per Months')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be031d5",
   "metadata": {},
   "source": [
    "# Data Pre-processing (Q4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e09fc81",
   "metadata": {},
   "source": [
    "Remove/Drop columns that are not essential for analysis and computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b45b44c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = ['days_in_waiting_list', 'arrival_date_year', 'arrival_date_year', 'assigned_room_type', 'booking_changes',\n",
    "               'reservation_status', 'country', 'days_in_waiting_list']\n",
    "df.drop(labels=label,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c9d06f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>is_canceled</th>\n",
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       "      <th>reservation_status_date</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>No Deposit</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-02</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-02</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>119385</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>394</td>\n",
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       "      <td>96.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-09-06</td>\n",
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       "    <tr>\n",
       "      <th>119386</th>\n",
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       "      <td>0</td>\n",
       "      <td>102</td>\n",
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>E</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>Transient</td>\n",
       "      <td>225.43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119387</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>Transient</td>\n",
       "      <td>157.71</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119388</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>89</td>\n",
       "      <td>Transient</td>\n",
       "      <td>104.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119389</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>Transient</td>\n",
       "      <td>151.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86560 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hotel  is_canceled  lead_time  arrival_date_month  \\\n",
       "0       Resort Hotel            0        342                   7   \n",
       "1       Resort Hotel            0        737                   7   \n",
       "2       Resort Hotel            0          7                   7   \n",
       "3       Resort Hotel            0         13                   7   \n",
       "4       Resort Hotel            0         14                   7   \n",
       "...              ...          ...        ...                 ...   \n",
       "119385    City Hotel            0         23                   8   \n",
       "119386    City Hotel            0        102                   8   \n",
       "119387    City Hotel            0         34                   8   \n",
       "119388    City Hotel            0        109                   8   \n",
       "119389    City Hotel            0        205                   8   \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",
       "...                          ...                        ...   \n",
       "119385                        35                         30   \n",
       "119386                        35                         31   \n",
       "119387                        35                         31   \n",
       "119388                        35                         31   \n",
       "119389                        35                         29   \n",
       "\n",
       "        stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n",
       "0                             0                     0       2         0  ...   \n",
       "1                             0                     0       2         0  ...   \n",
       "2                             0                     1       1         0  ...   \n",
       "3                             0                     1       1         0  ...   \n",
       "4                             0                     2       2         0  ...   \n",
       "...                         ...                   ...     ...       ...  ...   \n",
       "119385                        2                     5       2         0  ...   \n",
       "119386                        2                     5       3         0  ...   \n",
       "119387                        2                     5       2         0  ...   \n",
       "119388                        2                     5       2         0  ...   \n",
       "119389                        2                     7       2         0  ...   \n",
       "\n",
       "        previous_cancellations previous_bookings_not_canceled  \\\n",
       "0                            0                              0   \n",
       "1                            0                              0   \n",
       "2                            0                              0   \n",
       "3                            0                              0   \n",
       "4                            0                              0   \n",
       "...                        ...                            ...   \n",
       "119385                       0                              0   \n",
       "119386                       0                              0   \n",
       "119387                       0                              0   \n",
       "119388                       0                              0   \n",
       "119389                       0                              0   \n",
       "\n",
       "       reserved_room_type deposit_type  agent  customer_type     adr  \\\n",
       "0                       C   No Deposit      0      Transient    0.00   \n",
       "1                       C   No Deposit      0      Transient    0.00   \n",
       "2                       A   No Deposit      0      Transient   75.00   \n",
       "3                       A   No Deposit    304      Transient   75.00   \n",
       "4                       A   No Deposit    240      Transient   98.00   \n",
       "...                   ...          ...    ...            ...     ...   \n",
       "119385                  A   No Deposit    394      Transient   96.14   \n",
       "119386                  E   No Deposit      9      Transient  225.43   \n",
       "119387                  D   No Deposit      9      Transient  157.71   \n",
       "119388                  A   No Deposit     89      Transient  104.40   \n",
       "119389                  A   No Deposit      9      Transient  151.20   \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",
       "...                            ...                       ...   \n",
       "119385                           0                         0   \n",
       "119386                           0                         2   \n",
       "119387                           0                         4   \n",
       "119388                           0                         0   \n",
       "119389                           0                         2   \n",
       "\n",
       "        reservation_status_date  \n",
       "0                    2015-07-01  \n",
       "1                    2015-07-01  \n",
       "2                    2015-07-02  \n",
       "3                    2015-07-02  \n",
       "4                    2015-07-03  \n",
       "...                         ...  \n",
       "119385               2017-09-06  \n",
       "119386               2017-09-07  \n",
       "119387               2017-09-07  \n",
       "119388               2017-09-07  \n",
       "119389               2017-09-07  \n",
       "\n",
       "[86560 rows x 25 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2241b932",
   "metadata": {},
   "source": [
    "Segmentation and segregration of dataframe into two categories: numerical and categorical data for the sake of easy control and data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ecc29258",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_label=['hotel',\n",
    " 'meal',\n",
    " 'market_segment',\n",
    " 'distribution_channel',\n",
    " 'reserved_room_type',\n",
    " 'deposit_type',\n",
    " 'customer_type',\n",
    " 'reservation_status_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "780a32b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>meal</th>\n",
       "      <th>market_segment</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Direct</td>\n",
       "      <td>C</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Direct</td>\n",
       "      <td>C</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Direct</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel meal market_segment distribution_channel reserved_room_type  \\\n",
       "0  Resort Hotel   BB         Direct               Direct                  C   \n",
       "1  Resort Hotel   BB         Direct               Direct                  C   \n",
       "2  Resort Hotel   BB         Direct               Direct                  A   \n",
       "3  Resort Hotel   BB      Corporate            Corporate                  A   \n",
       "4  Resort Hotel   BB      Online TA                TA/TO                  A   \n",
       "\n",
       "  deposit_type customer_type reservation_status_date  \n",
       "0   No Deposit     Transient              2015-07-01  \n",
       "1   No Deposit     Transient              2015-07-01  \n",
       "2   No Deposit     Transient              2015-07-02  \n",
       "3   No Deposit     Transient              2015-07-02  \n",
       "4   No Deposit     Transient              2015-07-03  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df = df[cat_label]\n",
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4ecfad90",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df['reservation_status_date'] = pd.to_datetime(cat_df['reservation_status_date'])\n",
    "\n",
    "cat_df['year'] = cat_df['reservation_status_date'].dt.year\n",
    "cat_df['month'] = cat_df['reservation_status_date'].dt.month\n",
    "cat_df['day'] = cat_df['reservation_status_date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1dbd96b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>meal</th>\n",
       "      <th>market_segment</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>reservation_status_date</th>\n",
       "      <th>year</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Direct</td>\n",
       "      <td>C</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
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       "      <td>Transient</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-02</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>Online TA</td>\n",
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       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>2015</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119385</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Offline TA/TO</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119386</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>E</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119387</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>D</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119388</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>BB</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119389</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>HB</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86560 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hotel meal market_segment distribution_channel  \\\n",
       "0       Resort Hotel   BB         Direct               Direct   \n",
       "1       Resort Hotel   BB         Direct               Direct   \n",
       "2       Resort Hotel   BB         Direct               Direct   \n",
       "3       Resort Hotel   BB      Corporate            Corporate   \n",
       "4       Resort Hotel   BB      Online TA                TA/TO   \n",
       "...              ...  ...            ...                  ...   \n",
       "119385    City Hotel   BB  Offline TA/TO                TA/TO   \n",
       "119386    City Hotel   BB      Online TA                TA/TO   \n",
       "119387    City Hotel   BB      Online TA                TA/TO   \n",
       "119388    City Hotel   BB      Online TA                TA/TO   \n",
       "119389    City Hotel   HB      Online TA                TA/TO   \n",
       "\n",
       "       reserved_room_type deposit_type customer_type reservation_status_date  \\\n",
       "0                       C   No Deposit     Transient              2015-07-01   \n",
       "1                       C   No Deposit     Transient              2015-07-01   \n",
       "2                       A   No Deposit     Transient              2015-07-02   \n",
       "3                       A   No Deposit     Transient              2015-07-02   \n",
       "4                       A   No Deposit     Transient              2015-07-03   \n",
       "...                   ...          ...           ...                     ...   \n",
       "119385                  A   No Deposit     Transient              2017-09-06   \n",
       "119386                  E   No Deposit     Transient              2017-09-07   \n",
       "119387                  D   No Deposit     Transient              2017-09-07   \n",
       "119388                  A   No Deposit     Transient              2017-09-07   \n",
       "119389                  A   No Deposit     Transient              2017-09-07   \n",
       "\n",
       "        year  month  day  \n",
       "0       2015      7    1  \n",
       "1       2015      7    1  \n",
       "2       2015      7    2  \n",
       "3       2015      7    2  \n",
       "4       2015      7    3  \n",
       "...      ...    ...  ...  \n",
       "119385  2017      9    6  \n",
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       "119387  2017      9    7  \n",
       "119388  2017      9    7  \n",
       "119389  2017      9    7  \n",
       "\n",
       "[86560 rows x 11 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df"
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  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b451b964",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df.drop(['reservation_status_date'] , axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "65ee88db",
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   "outputs": [
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      "text/plain": [
       "               hotel meal market_segment distribution_channel  \\\n",
       "0       Resort Hotel   BB         Direct               Direct   \n",
       "1       Resort Hotel   BB         Direct               Direct   \n",
       "2       Resort Hotel   BB         Direct               Direct   \n",
       "3       Resort Hotel   BB      Corporate            Corporate   \n",
       "4       Resort Hotel   BB      Online TA                TA/TO   \n",
       "...              ...  ...            ...                  ...   \n",
       "119385    City Hotel   BB  Offline TA/TO                TA/TO   \n",
       "119386    City Hotel   BB      Online TA                TA/TO   \n",
       "119387    City Hotel   BB      Online TA                TA/TO   \n",
       "119388    City Hotel   BB      Online TA                TA/TO   \n",
       "119389    City Hotel   HB      Online TA                TA/TO   \n",
       "\n",
       "       reserved_room_type deposit_type customer_type  year  month  day  \n",
       "0                       C   No Deposit     Transient  2015      7    1  \n",
       "1                       C   No Deposit     Transient  2015      7    1  \n",
       "2                       A   No Deposit     Transient  2015      7    2  \n",
       "3                       A   No Deposit     Transient  2015      7    2  \n",
       "4                       A   No Deposit     Transient  2015      7    3  \n",
       "...                   ...          ...           ...   ...    ...  ...  \n",
       "119385                  A   No Deposit     Transient  2017      9    6  \n",
       "119386                  E   No Deposit     Transient  2017      9    7  \n",
       "119387                  D   No Deposit     Transient  2017      9    7  \n",
       "119388                  A   No Deposit     Transient  2017      9    7  \n",
       "119389                  A   No Deposit     Transient  2017      9    7  \n",
       "\n",
       "[86560 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
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    }
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    "cat_df"
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  },
  {
   "cell_type": "markdown",
   "id": "f0b6300f",
   "metadata": {},
   "source": [
    "Converting Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f526c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df['hotel'] = cat_df['hotel'].map({'Resort Hotel' : 0, 'City Hotel' : 1})\n",
    "\n",
    "cat_df['meal'] = cat_df['meal'].map({'BB' : 0, 'FB': 1, 'HB': 2, 'SC': 3, 'Undefined': 4})\n",
    "\n",
    "cat_df['market_segment'] = cat_df['market_segment'].map({'Direct': 0, 'Corporate': 1, 'Online TA': 2, 'Offline TA/TO': 3,\n",
    "                                                           'Complementary': 4, 'Groups': 5, 'Undefined': 6, 'Aviation': 7})\n",
    "\n",
    "cat_df['distribution_channel'] = cat_df['distribution_channel'].map({'Direct': 0, 'Corporate': 1, 'TA/TO': 2, 'Undefined': 3,\n",
    "                                                                       'GDS': 4})\n",
    "\n",
    "cat_df['reserved_room_type'] = cat_df['reserved_room_type'].map({'C': 0, 'A': 1, 'D': 2, 'E': 3, 'G': 4, 'F': 5, 'H': 6,\n",
    "                                                                   'L': 7, 'B': 8})\n",
    "\n",
    "cat_df['deposit_type'] = cat_df['deposit_type'].map({'No Deposit': 0, 'Refundable': 1, 'Non Refund': 3})\n",
    "\n",
    "cat_df['customer_type'] = cat_df['customer_type'].map({'Transient': 0, 'Contract': 1, 'Transient-Party': 2, 'Group': 3})\n",
    "\n",
    "cat_df['year'] = cat_df['year'].map({2015: 0, 2014: 1, 2016: 2, 2017: 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fb191e2a",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   hotel  meal  market_segment  distribution_channel  reserved_room_type  \\\n",
       "0      0     0               0                     0                   0   \n",
       "1      0     0               0                     0                   0   \n",
       "2      0     0               0                     0                   1   \n",
       "3      0     0               1                     1                   1   \n",
       "4      0     0               2                     2                   1   \n",
       "\n",
       "   deposit_type  customer_type  year  month  day  \n",
       "0             0              0     0      7    1  \n",
       "1             0              0     0      7    1  \n",
       "2             0              0     0      7    2  \n",
       "3             0              0     0      7    2  \n",
       "4             0              0     0      7    3  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e514f32e",
   "metadata": {},
   "outputs": [
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       "      <td>96.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119386</th>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>225.43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119387</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>157.71</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119388</th>\n",
       "      <td>109</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>104.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119389</th>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>151.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86560 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lead_time  arrival_date_month  arrival_date_week_number  \\\n",
       "0             342                   7                        27   \n",
       "1             737                   7                        27   \n",
       "2               7                   7                        27   \n",
       "3              13                   7                        27   \n",
       "4              14                   7                        27   \n",
       "...           ...                 ...                       ...   \n",
       "119385         23                   8                        35   \n",
       "119386        102                   8                        35   \n",
       "119387         34                   8                        35   \n",
       "119388        109                   8                        35   \n",
       "119389        205                   8                        35   \n",
       "\n",
       "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
       "0                               1                        0   \n",
       "1                               1                        0   \n",
       "2                               1                        0   \n",
       "3                               1                        0   \n",
       "4                               1                        0   \n",
       "...                           ...                      ...   \n",
       "119385                         30                        2   \n",
       "119386                         31                        2   \n",
       "119387                         31                        2   \n",
       "119388                         31                        2   \n",
       "119389                         29                        2   \n",
       "\n",
       "        stays_in_week_nights  adults  children  babies  is_repeated_guest  \\\n",
       "0                          0       2         0       0                  0   \n",
       "1                          0       2         0       0                  0   \n",
       "2                          1       1         0       0                  0   \n",
       "3                          1       1         0       0                  0   \n",
       "4                          2       2         0       0                  0   \n",
       "...                      ...     ...       ...     ...                ...   \n",
       "119385                     5       2         0       0                  0   \n",
       "119386                     5       3         0       0                  0   \n",
       "119387                     5       2         0       0                  0   \n",
       "119388                     5       2         0       0                  0   \n",
       "119389                     7       2         0       0                  0   \n",
       "\n",
       "        previous_cancellations  previous_bookings_not_canceled  agent     adr  \\\n",
       "0                            0                               0      0    0.00   \n",
       "1                            0                               0      0    0.00   \n",
       "2                            0                               0      0   75.00   \n",
       "3                            0                               0    304   75.00   \n",
       "4                            0                               0    240   98.00   \n",
       "...                        ...                             ...    ...     ...   \n",
       "119385                       0                               0    394   96.14   \n",
       "119386                       0                               0      9  225.43   \n",
       "119387                       0                               0      9  157.71   \n",
       "119388                       0                               0     89  104.40   \n",
       "119389                       0                               0      9  151.20   \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",
       "...                             ...                        ...  \n",
       "119385                            0                          0  \n",
       "119386                            0                          2  \n",
       "119387                            0                          4  \n",
       "119388                            0                          0  \n",
       "119389                            0                          2  \n",
       "\n",
       "[86560 rows x 16 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df = df.drop(columns = cat_label, axis = 1)\n",
    "num_df.drop('is_canceled', axis = 1, inplace = True)\n",
    "num_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0b380052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lead_time                          7405.514793\n",
       "arrival_date_month                    9.555541\n",
       "arrival_date_week_number            186.220567\n",
       "arrival_date_day_of_month            78.056524\n",
       "stays_in_weekend_nights               1.049666\n",
       "stays_in_week_nights                  4.125836\n",
       "adults                                0.377861\n",
       "children                              0.200278\n",
       "babies                                0.012925\n",
       "is_repeated_guest                     0.037300\n",
       "previous_cancellations                0.137048\n",
       "previous_bookings_not_canceled        2.962229\n",
       "agent                             12111.852271\n",
       "adr                                3009.694782\n",
       "required_car_parking_spaces           0.078853\n",
       "total_of_special_requests             0.691541\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283c9d15",
   "metadata": {},
   "source": [
    "some column has distinguishly higher varience. So, there is a need for us to normalize and rescale them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "20aa71fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df['lead_time'] = np.log(num_df['lead_time'] + 1)\n",
    "num_df['arrival_date_week_number'] = np.log(num_df['arrival_date_week_number'] + 1)\n",
    "num_df['arrival_date_day_of_month'] = np.log(num_df['arrival_date_day_of_month'] + 1)\n",
    "num_df['agent'] = np.log(num_df['agent'] + 1)\n",
    "num_df['adr'] = np.log(num_df['adr'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6ca0d3ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lead_time                         2.631912\n",
       "arrival_date_month                9.555541\n",
       "arrival_date_week_number          0.455235\n",
       "arrival_date_day_of_month         0.513531\n",
       "stays_in_weekend_nights           1.049666\n",
       "stays_in_week_nights              4.125836\n",
       "adults                            0.377861\n",
       "children                          0.200278\n",
       "babies                            0.012925\n",
       "is_repeated_guest                 0.037300\n",
       "previous_cancellations            0.137048\n",
       "previous_bookings_not_canceled    2.962229\n",
       "agent                             3.471362\n",
       "adr                               0.614435\n",
       "required_car_parking_spaces       0.078853\n",
       "total_of_special_requests         0.691541\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9af0cdd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_df['adr'] = num_df['adr'].fillna(value = num_df['adr'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e937699d",
   "metadata": {},
   "source": [
    "# Constructing Machine Learning Models (Q4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ac844372",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([cat_df, num_df], axis = 1)\n",
    "y = df['is_canceled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "55f8d8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32db677a",
   "metadata": {},
   "source": [
    "Data split into traing set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d466f32a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size=0.30,random_state=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0876b7b0",
   "metadata": {},
   "source": [
    "Logistic Regresssion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4cb6f3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score of Decision Tree is : 0.7993684534812077\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "\n",
    "acc_lr = accuracy_score(y_test, y_pred_lr)\n",
    "conf = confusion_matrix(y_test, y_pred_lr)\n",
    "clf_report = classification_report(y_test, y_pred_lr)\n",
    "\n",
    "print(f\"Accuracy Score of Decision Tree is : {acc_lr}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "96ede090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "98c4a52c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.95      0.87     18830\n",
      "           1       0.76      0.40      0.52      7138\n",
      "\n",
      "    accuracy                           0.80     25968\n",
      "   macro avg       0.78      0.67      0.70     25968\n",
      "weighted avg       0.79      0.80      0.78     25968\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(clf_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c17699c",
   "metadata": {},
   "source": [
    "Classifier for Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "88ce3b15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score of Decision Tree is : 0.9344192852741836\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(X_train, y_train)\n",
    "\n",
    "y_pred_dtc = dtc.predict(X_test)\n",
    "\n",
    "acc_dtc = accuracy_score(y_test, y_pred_dtc)\n",
    "conf = confusion_matrix(y_test, y_pred_dtc)\n",
    "clf_report = classification_report(y_test, y_pred_dtc)\n",
    "\n",
    "print(f\"Accuracy Score of Decision Tree is : {acc_dtc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4ca4441a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3f565bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.96      0.95     18830\n",
      "           1       0.88      0.88      0.88      7138\n",
      "\n",
      "    accuracy                           0.93     25968\n",
      "   macro avg       0.92      0.92      0.92     25968\n",
      "weighted avg       0.93      0.93      0.93     25968\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(clf_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50170e6e",
   "metadata": {},
   "source": [
    "Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d0cad4f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score of Random Forest is : 0.9411198398028343\n"
     ]
    }
   ],
   "source": [
    "rd_clf = RandomForestClassifier()\n",
    "rd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rd_clf = rd_clf.predict(X_test)\n",
    "\n",
    "acc_rd_clf = accuracy_score(y_test, y_pred_rd_clf)\n",
    "conf = confusion_matrix(y_test, y_pred_rd_clf)\n",
    "clf_report = classification_report(y_test, y_pred_rd_clf)\n",
    "\n",
    "print(f\"Accuracy Score of Random Forest is : {acc_rd_clf}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8054acef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD4CAYAAAAn3bdmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAe/0lEQVR4nO3deXwV5fXH8c9JWEWCLIqQICqgVrSguNal1g1QC7hgo1VshaZFbV3a/iy1VaviLihW0FAUwSJFUUEEBVfEIgoWRVRKQCqByL65FJJ7z++PO6EXuLm5JIEw4/fN63llcmZ7Lq9weHLmmRlzd0REJByyarsDIiKSOSVtEZEQUdIWEQkRJW0RkRBR0hYRCZE6u/oEpasXa3qK7KBh61NquwuyByrbssyqe4ydyTl1Wxxc7fPtbrs8aYuI7FbxWG33YJdS0haRaPF4bfdgl1LSFpFoiStpi4iEhmukLSISIrGy2u7BLqWkLSLRoguRIiIhovKIiEiI6EKkiEh46EKkiEiYRHykrWePiEi0xEozb5Uws8fNbKWZfZwU+4eZzQ3aEjObG8QPNLNvk9Y9mrRPFzObZ2ZFZjbEzCyI1w+OV2Rms8zswMr6pKQtItHi8cxb5UYC3bY5vPtP3L2zu3cGxgPPJa1eVL7O3X+VFB8GFAAdglZ+zL7AOndvDwwG7qmsQ0raIhIt8XjmrRLuPh1Ym2pdMFq+GHg63THMrBWQ4+4zPfF+x1FAr2B1T+DJYPlZ4IzyUXhFlLRFJFpqdqSdzinACndfmBQ7yMz+ZWZvmVn5oyxzgeKkbYqDWPm6pQDuXgZsAJqnO6kuRIpItOzEhUgzKyBRtihX6O6FGe5+CduOskuAA9x9jZl1AV4ws45AqpFz+eNj061LSUlbRCLF45VfYNy6bSJBZ5qktzKzOsAFQJekY20GNgfLc8xsEXAIiZF1XtLuecDyYLkYaAMUB8dsQgXlmHIqj4hItNRgTTuNM4HP3H1r2cPM9jWz7GD5YBIXHBe7ewmwycxOCOrVfYAJwW4TgSuC5YuA14O6d4U00haRaKnBm2vM7GngNKCFmRUDt7j7CCCfHS9AngrcZmZlQAz4lbuXj5r7k5iJ0hCYEjSAEcBoMysiMcLOr7RPlST1atPrxiQVvW5MUqmJ14399/3xGeecBsdeqNeNiYjUKt3GLiISIhG/jV1JW0SiRS9BEBEJEY20RUTCw11vrhERCQ+NtEVEQkSzR0REQkQjbRGRENHsERGREFF5REQkRFQeEREJESVtEZEQUXlERCREdCFSRCREVB4REQkRlUdEREJEI20RkRBR0hYRCZFd/ArF2qakLSLRUqbZIyIi4RHxC5FZtd0BEZEaFY9n3iphZo+b2Uoz+zgpdquZLTOzuUE7J2ndADMrMrMFZtY1Kd7FzOYF64aYmQXx+mb2jyA+y8wOrKxPStoiEi3umbfKjQS6pYgPdvfOQZsMYGaHA/lAx2CfoWaWHWw/DCgAOgSt/Jh9gXXu3h4YDNxTWYeUtEUkWmpwpO3u04G1GZ65JzDW3Te7++dAEXCcmbUCctx9prs7MArolbTPk8Hys8AZ5aPwiihpi0i07ETSNrMCM5ud1AoyPMs1ZvZRUD5pGsRygaVJ2xQHsdxgefv4Nvu4exmwAWie7sRK2iISKR6LZd7cC939mKRWmMEphgHtgM5ACfBAEE81QvY08XT7VEhJW0SipQbLI6m4+wp3j7l7HBgOHBesKgbaJG2aBywP4nkp4tvsY2Z1gCZUUo5R0haRaPF45q0Kghp1ufOB8pklE4H8YEbIQSQuOL7n7iXAJjM7IahX9wEmJO1zRbB8EfB6UPeukOZpi0i0xGvujkgzexo4DWhhZsXALcBpZtaZRBljCfBLAHefb2bjgE+AMuBqd48Fh+pPYiZKQ2BK0ABGAKPNrIjECDu/sj4paYtItNTgs0fc/ZIU4RFpth8IDEwRnw0ckSL+X6D3zvRJSVtEoiUWq3ybEFNNO40/3TmIU8/Np9dlv0q5fsPGTfxmwG2c36c/+f2uZeHiJdU+55YtW/jtn++i+8VXcskvrmNZyYpt1n/19dec3vMyBj4wtNrnkqoZXvgAy4s/ZO6/Xku5/tBD2zFj+kS+3rSYG67/ZY2cs169eoz5+zA++2QG/5zxIm3bJq5rderUkRnTJ/Lh3Nf5YM40evfuUSPnC7VdfCGytilpp9HrnLN4dNAdFa4fPuofHNahHc+PGsadf/4ddz/4aMbHXlaygp9d8387xJ+bNJWcxnszZdzjXP6TXgwa+vg26x8ePppjjjoy8w8hNW7UqHGce95PK1y/du16rrv+zwwa/NhOH7tt2zxem/bMDvErf34J69Zt4LDDT+bBIcO5686bAPjmm2/52ZXX0qnz6Zx73mUMuv9WmjTJ2enzRkrcM28hpKSdxjGdj6RJTuMK1y9a8gUndOkEwMFt27CsZAWr164D4MVXXie/37VceMXV/OXeIcQy/JXt9bdn0vOcMwE4+7RTmDVnLuUXk+d/tpA1a9fxg2OPrs7Hkmp6e8Ys1q5bX+H6VavWMHvOh5SWlu6w7tJLL2DmO5OY/f5Uhj5yD1lZmf0T7PHjsxk9OpHMx49/idN/dDIACxcupqjocwBKSlawctUa9t037b0Z0beLZ4/Utkp/YszsMDO7MXjIyUPB8vd2R+f2dIe2P5hX3/onAPM+WUDJipWsWLmaRUu+4OXX3mL0ow8w/slHyMrKYtLUNzI65spVa9h/vxYA1KmTzd6N9mL9ho3E43Hu++twfnt1v132eWTXOuyw9lzcuwen/LAXxxx7NrFYjEsvvSCjfVvn7s/S4sTU3lgsxoYNG2nevOk22xx7TGfq1avLokVLarrr4RLxkXbaC5FmdiNwCTAWeC8I5wFPm9lYd7+7gv0KSDwchaEP3EG/PqkuwIZfv8t7c/eDj3HhFVfTod2BHNahHdnZ2cyaPZdPPisiv++1AGzevJlmTfcB4DcDbmPZ8hWUlpVSsmIVF15xNQCXXdyT8889m1RTNM2Msc9N4tQTj6VVy3132+eTmnX6j07m6KOO5N2ZkwFo2LABq1atBuDZZ/7GgQceQL16dTmgTS6z358KwMMP/40nR40j1eMokn9U9t9/P0aOHMKVV16X8mfou8RDWqvOVGWzR/oCHd19m9/zzGwQMB9ImbSDW0ELAUpXL47sT9DejRpxx003AODudL3oZ+S1bsmcufPo0f1Mru//8x32GXLXzUCipn3TwAcY+dd7t1nfcr8WfLlyNfvvty9lZTG++vobmuQ05sOPP2XOR/MZ+9wkvvn2v5SWlrLXXg24vv+Vu/6DSo0wM0Y/9Qw3/WnHfzYX9U78BtW2bR6P/20wZ5y17SywZcUltMlrzbJlJWRnZ9OkSQ5rg1Jc48Z7M3HCKG6+5V5mvffBrv8ge7rv+OyRONA6RbxVsO47beOmr7bWLce/+DJdOh/J3o0accIxnZn25gzWBHXPDRs3sfzLFWmO9D8/OvkEJkx+FYCpb77N8V06YWbcc+uNvPrcKKaOf5LfXd2PHt3OVMIOmdffmMEF55+3tebctOk+HHBAbiV7Jbw4aSqXX55I5BdeeC5vvPkOAHXr1mX8MyN46qlnGT9+0q7peNh8l8sjwHXAa2a2kP89veoAoD1wzS7s1x7h97fczfv/+oj16zdyRq/LuKrv5ZQFrzL6yfnnsvg/S/nj7feTnZXFwQcewG0DrgOg3UFt+fUv+lBw3U3EPU7dOnW46YaraL1/y0rPecF5XRlw+310v/hKmuQ05r6//GFXfkSpgqdGP8IPTz2RFi2asWTxbP5y2/3UrVsXgMLho2nZcl9mzZxCTs7exONxfvPrX3Bkp9P49NOF3HzrvUyZ/DRZWUZpaRm/+c1NfPHFskrP+fgTY3ly5BA++2QG69at59LLrgKgd+8fc8opx9OseVP69LkYgL79rufDD+fvur+APV3EyyNWWf3LzLJIPBAll8QTqYqB95Nuz0wryuURqbqGrU+p7S7IHqhsy7K0z5LOxNc352eccxrdNrba59vdKr0jMniS1bu7oS8iItUX0ql8mdJt7CISLSGtVWdKSVtEIsXLoj17RElbRKJFI20RkRBRTVtEJEQ00hYRCQ9X0hYRCRFdiBQRCRGNtEVEQiTiSVsvQRCRSHH3jFtlzOxxM1tpZh8nxe4zs8/M7CMze97M9gniB5rZt2Y2N2iPJu3TxczmmVlR8G4CC+L1zewfQXyWmR1YWZ+UtEUkWmr2KX8jgW7bxaYBR7j794F/AwOS1i1y985BS3657DAS7xjoELTyY/YF1rl7e2AwcE9lHVLSFpFoqcGk7e7TgbXbxaa6e1nw7bskXgxTITNrBeS4+0xPDO9HAb2C1T2BJ4PlZ4EzLNUbL5IoaYtIpHhZPONmZgVmNjupFezk6a4EpiR9f5CZ/cvM3jKz8kdZ5pJ4Omq54iBWvm4pQPAfwQYg7Us+dSFSRKJlJ26ITH7L1s4ys5uAMuDvQagEOMDd15hZF+AFM+tI4pHWO5y6/DBp1qWkpC0ikbI7bq4xsyuA84AzgpIH7r4Z2BwszzGzRcAhJEbWySWUPGB5sFwMtAGKzawO0ITtyjHbU3lERKJlF79uzMy6ATcCPdz9m6T4vmaWHSwfTOKC42J3LwE2mdkJQb26DzAh2G0icEWwfBHwulcyrUUjbRGJlhp8XpSZPQ2cBrQws2LgFhKzReoD04Jrhu8GM0VOBW4zszIgBvzK3ctHzf1JzERpSKIGXl4HHwGMNrMiEiPs/Er7lMlcxerQ68YkFb1uTFKpideNret9WsY5p+kzb0bvdWMiImHiZdEeJyppi0i0RPtx2kraIhItEX8HgpK2iESMkraISHhopC0iEiJbnwoSUUraIhIpGmmLiISIkraISJh46O6X2SlK2iISKRppi4iEiMc10hYRCY14TElbRCQ0VB4REQkRlUdEREJkFz9tutYpaYtIpGikLSISIroQKSISIhppi4iEiOuOSBGR8Ij6lL+s2u6AiEhNirtl3CpjZo+b2Uoz+zgp1szMppnZwuBr06R1A8ysyMwWmFnXpHgXM5sXrBtiwWvczay+mf0jiM8yswMr65OStohEirtl3DIwEui2XewPwGvu3gF4LfgeMzscyAc6BvsMNbPsYJ9hQAHQIWjlx+wLrHP39sBg4J7KOqSkLSKREo9Zxq0y7j4dWLtduCfwZLD8JNArKT7W3Te7++dAEXCcmbUCctx9prs7MGq7fcqP9SxwRvkovCJK2iISKR63jJuZFZjZ7KRWkMEpWrp7CUDwdb8gngssTdquOIjlBsvbx7fZx93LgA1A83Qn14VIEYmUTGrV5dy9ECisoVOnOrGniafbp0IaaYtIpNRwTTuVFUHJg+DryiBeDLRJ2i4PWB7E81LEt9nHzOoATdixHLMNJW0RiRT3zFsVTQSuCJavACYkxfODGSEHkbjg+F5QQtlkZicE9eo+2+1TfqyLgNeDuneFVB4RkUjZmfJIZczsaeA0oIWZFQO3AHcD48ysL/AF0BvA3eeb2TjgE6AMuNrdY8Gh+pOYidIQmBI0gBHAaDMrIjHCzq+0T5Uk9WorXb044s/ckqpo2PqU2u6C7IHKtiyrdsb9oE3PjHPO0UsnhO72SY20RSRSanKkvSfa5Un7oEN67OpTSAj9tvWptd0FiSg9e0REJEQ00hYRCZGoX0RT0haRSInFoz2TWUlbRCIl4k9mVdIWkWjxlHeGR4eStohESjziRW0lbRGJlLhG2iIi4aHyiIhIiMSUtEVEwkOzR0REQkRJW0QkRFTTFhEJkXi0c7aStohEi6b8iYiESKzyTUJNSVtEIiVuGmmLiIRGxO9iV9IWkWiJ+pS/aD94VkS+c+KWeUvHzA41s7lJbaOZXWdmt5rZsqT4OUn7DDCzIjNbYGZdk+JdzGxesG6IWdVrOEraIhIpMSzjlo67L3D3zu7eGegCfAM8H6weXL7O3ScDmNnhQD7QEegGDDWz7GD7YUAB0CFo3ar6+ZS0RSRSamqkvZ0zgEXu/p802/QExrr7Znf/HCgCjjOzVkCOu890dwdGAb2q9umUtEUkYuI70cyswMxmJ7WCCg6bDzyd9P01ZvaRmT1uZk2DWC6wNGmb4iCWGyxvH68SJW0RiRTfmeZe6O7HJLXC7Y9nZvWAHsAzQWgY0A7oDJQAD5RvWkF3KopXiWaPiEik7ILb2LsDH7j7CoDyrwBmNhyYFHxbDLRJ2i8PWB7E81LEq0QjbRGJlJ0pj2ToEpJKI0GNutz5wMfB8kQg38zqm9lBJC44vufuJcAmMzshmDXSB5hQlc8GGmmLSMTEanCkbWZ7AWcBv0wK32tmnUmUOJaUr3P3+WY2DvgEKAOudvfyu+r7AyOBhsCUoFWJkraIREpN3lzj7t8AzbeLXZ5m+4HAwBTx2cARNdEnJW0RiZSo3xGppC0ikaJnj4iIhIhegiAiEiIqj4iIhIhegiAiEiIqj4iIhIjKIyIiIaLZIyIiIRKPeNpW0haRSNGFSBGREFFNW0QkRDR7REQkRFTTFhEJkWinbCVtEYkY1bRFREIkFvGxtpK2iESKRtoiIiGiC5EiIiES7ZStpC0iERP18khWbXdARKQmxfCMW2XMbImZzTOzuWY2O4g1M7NpZrYw+No0afsBZlZkZgvMrGtSvEtwnCIzG2JmVb4FSElbRCIljmfcMvQjd+/s7scE3/8BeM3dOwCvBd9jZocD+UBHoBsw1Myyg32GAQVAh6B1q+rnU3kkjfsfvp0zzz6V1avXcuZJ51e4XaejjmDi1L9zVd/f8dLEadU6Z716dXlw2F18v9PhrFu3nv5X/o7ipcvJzWvF8FEPkp2dTZ26dXiicAxPjRxXrXNJ1dw4Ywibv/oWj8eJl8V5uMdN26w/teA8jup1EgBZ2dns1z6X244u4NsNX1f5nNn16vCTQVeRe8RBfLP+K8Zc8xDrilezT24LLn/0erKys8iuU4d3nnyFWX9/tVqfL+x2Q027J3BasPwk8CZwYxAf6+6bgc/NrAg4zsyWADnuPhPAzEYBvYApVTm5knYaz4x5gZHDx/DgsDsr3CYrK4s/3nI9b73+zk4dO69NawY/MpDePX6+TTz/sgvYsH4jJx9zDj0u6M4fb72Bq/r+jpUrVtGr22Vs2VLKXo0a8to7LzDt5TdY8eWqKn02qZ7CS+7gm3WbUq6bXjiJ6YWTAPjeGUdzct9zMk7YTfNa0Pv+/hTm375N/NiLf8S3G77mvtOup9OPT6T7Hy5lzDVD2LRyHUMvvIXYljLq7VWf66fexyfT5rBp5brqfcAQq+HZIw5MNTMHHnP3QqClu5cAuHuJme0XbJsLvJu0b3EQKw2Wt49XicojacyaOYf16zak3ebnBZcy+cVprF61dpv4Bb3PY9K0p3nlrWe5e9DNZGVl9ld99jmn88zYCQC8NGEqJ596PAClpWVs2VIKQL169TI+ntSuTj1+wIcT/7n1+6N6ncw1L9zOtZPv4oI7+2JZmZU2O57dhTnjpwMwb/Is2v/gCABipTFiW8oAqFOvLllVL5VGRnwnmpkVmNnspFaw3eFOcvejge7A1WZ2appTp/rL9zTxKtG//GrYv9V+dD/3DEY/sW2Zov0hB/Pj87vRq/vldP3hRcRicc7vfV7GxyxZ9iUAsViMjRu/ommzfQBolbs/095+jvfnvcrQh0ZolF1b3Ok3egC/fnEgx11yeoWb1W1Qj0N/2Il5U2YBsF+71nz/vBMYetGtPHTOAOIx56heJ2d0ypyWzdiwfA0A8Vic/276hr2aNgagSatmXDflHgbM/CtvPjrxOz3KBvCd+eNe6O7HJLXCbY7lvjz4uhJ4HjgOWGFmrQCCryuDzYuBNkm75wHLg3heiniVVLk8YmY/d/cnKlhXQKLozj57taJR/WZVPc0e7dY7b+TOvwwmHt92ktHJpx7PkZ0O56XXxgLQoEF91qxOjMT/Nuoh2rTNpW69uuTmtuKVt54FYMRjTzFuzAukuqjsnvhPuWTZl5x1ygW03H9fRowewksTp7F61Zpd+RElhaEX3sqmleto1DyHfk/9kVWLlvP5e5/tsN33zjyaJbMXbC2NtDvpCPKOPJhfT7wDgLr16/HVmsRvcpc/dgPN2uxLdt067NO6BddOvguAd554mdnPvJXy54Lg52JDyVoe7H4jjfdryhWFNzBvynt8tTr9b4hRVlO3sZtZIyDL3TcFy2cDtwETgSuAu4OvE4JdJgJjzGwQ0JrEBcf33D1mZpvM7ARgFtAHeLiq/apOTfsvQMqkHfxvVQiQ1+yIyM51/37njjzyt/sAaNasKaefdQplZTHMjGfHTuTu2x/cYZ9+fa4FKq5plyxfQavc/SlZvoLs7GxycvbeoUSz4stVLFhQxPEnHl3tC5+y88pHsl+v2cj8V96nTad2KZN2px9vWxoxM+aMn87L947dYdvRvxwEVFzT3vDlGpq0bs6GL9eSlZ1Fg8Z78c36r3bo14qFxRx07KHMm/JetT9nWNXgPO2WwPPBf5h1gDHu/rKZvQ+MM7O+wBdAbwB3n29m44BPgDLgancvf5FOf2Ak0JDEBcgqXYSESsojZvZRBW1e8IG+035wVDdO7NyVEzt35aWJU7np93fwyuTXmTH9Xc7tcRbNWyR+w9hnnxxy81pldMxpU96gd35PAM7teTbvvJ341bpV65Y0aFAfgCZNcjj2uKNYtHBJzX8oSatuw/rUa9Rg6/Ihp3yfL/9dvMN2DRo35ODjv8f8aXO2xore+Zgjux9Ho+Y5ADRs0oh9cltkdN5Pps2hy4WJcuqR5xzPon/OB6DJ/s2oU79u4ng5jWjb5VBWLS6p+geMgLh7xi0dd1/s7p2C1tHdBwbxNe5+hrt3CL6uTdpnoLu3c/dD3X1KUny2ux8RrLvGvZKTp1HZSLsl0BXYvkhmwD933Dxa/jr8Xk486ViaNd+H9z9+lQfuHkqdOom/snTT7RYuWMy9dz7MmPGFZGVlUVpayp/+byDLiiv/xzT2qed46NG7mDF7MuvXbeCqfr8HEnXym2//Pe6OmfHYIyP57NOFNfNBJWONWzTh8sIbAMjOzuZfE97h3299yPE/PRNg63S7jl2PZeHbH1H67eat+64sWsYrD4yj3+gBmGURKytjws1PsH7Z6krP+/64N/nJoKv4/ZuD+Xb9V4z5deK36/3a53LuTZfhOIYxffgkvlywtKY/dqhE9lf7gKVL+GY2AnjC3WekWDfG3S+t7ARRLo9I1f0058ja7oLsge5Z8nS1p79c2vb8jHPOmP88H7rpNmlH2u7eN826ShO2iMju5hEfa+vmGhGJlDIlbRGR8NBIW0QkRKL+aFYlbRGJlGrMpgsFJW0RiRS9bkxEJET0NnYRkRDRSFtEJERU0xYRCRHNHhERCRHN0xYRCRHVtEVEQiTm0S6QKGmLSKSoPCIiEiKVvdwg7JS0RSRSop2ylbRFJGJ0IVJEJESUtEVEQiTqs0fSvo1dRCRsfCf+pGNmbczsDTP71Mzmm9m1QfxWM1tmZnODdk7SPgPMrMjMFphZ16R4FzObF6wbYmZVfjelRtoiEik1+OyRMuC37v6BmTUG5pjZtGDdYHe/P3ljMzscyAc6Aq2BV83sEHePAcOAAuBdYDLQDZhSlU5ppC0ikRLHM27puHuJu38QLG8CPgVy0+zSExjr7pvd/XOgCDjOzFoBOe4+0xP/o4wCelX18ylpi0ikuHvGzcwKzGx2UitIdUwzOxA4CpgVhK4xs4/M7HEzaxrEcoGlSbsVB7HcYHn7eJUoaYtIpMSIZ9zcvdDdj0lqhdsfz8z2BsYD17n7RhKljnZAZ6AEeKB80xTd8TTxKlFNW0QipSbviDSzuiQS9t/d/TkAd1+RtH44MCn4thhok7R7HrA8iOeliFeJRtoiEik1OHvEgBHAp+4+KCneKmmz84GPg+WJQL6Z1Tezg4AOwHvuXgJsMrMTgmP2ASZU9fNppC0ikVKDI+2TgMuBeWY2N4j9EbjEzDqTKHEsAX4J4O7zzWwc8AmJmSdXBzNHAPoDI4GGJGaNVGnmCChpi0jE1NRT/tx9Bqnr0ZPT7DMQGJgiPhs4oib6paQtIpGip/yJiIRI1G9jV9IWkUjRSxBERELENdIWEQkPPZpVRCREavCBUXskJW0RiRSNtEVEQiQWV01bRCQ0NHtERCREVNMWEQkR1bRFREJEI20RkRDRhUgRkRBReUREJERUHhERCRE9mlVEJEQ0T1tEJEQ00hYRCZG4Hs0qIhIeuhApIhIiStoiIiES7ZQNFvX/lfYkZlbg7oW13Q/Zs+jnQnZGVm134DumoLY7IHsk/VxIxpS0RURCRElbRCRElLR3L9UtJRX9XEjGdCFSRCRENNIWEQkRJW0RkRBR0t5NzKybmS0wsyIz+0Nt90dqn5k9bmYrzezj2u6LhIeS9m5gZtnAI0B34HDgEjM7vHZ7JXuAkUC32u6EhIuS9u5xHFDk7ovdfQswFuhZy32SWubu04G1td0PCRcl7d0jF1ia9H1xEBMR2SlK2ruHpYhprqWI7DQl7d2jGGiT9H0esLyW+iIiIaakvXu8D3Qws4PMrB6QD0ys5T6JSAgpae8G7l4GXAO8AnwKjHP3+bXbK6ltZvY0MBM41MyKzaxvbfdJ9ny6jV1EJEQ00hYRCRElbRGREFHSFhEJESVtEZEQUdIWEQkRJW0RkRBR0hYRCZH/B4oDDxX2GOf3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "adb9e7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96     18830\n",
      "           1       0.98      0.80      0.88      7138\n",
      "\n",
      "    accuracy                           0.94     25968\n",
      "   macro avg       0.96      0.90      0.92     25968\n",
      "weighted avg       0.94      0.94      0.94     25968\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(clf_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d2ef4704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.941120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.934419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.799368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model     Score\n",
       "2  Random Forest Classifier  0.941120\n",
       "1  Decision Tree Classifier  0.934419\n",
       "0       Logistic Regression  0.799368"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model' : ['Logistic Regression', 'Decision Tree Classifier', 'Random Forest Classifier'],\n",
    "    'Score' : [acc_lr,acc_dtc, acc_rd_clf]\n",
    "})\n",
    "\n",
    "\n",
    "models.sort_values(by ='Score', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959ea817",
   "metadata": {},
   "source": [
    "# Q1. Provide various descriptive analytics and insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4a48d189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City Hotel      53048\n",
       "Resort Hotel    33512\n",
       "Name: hotel, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hotel.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5028ce5",
   "metadata": {},
   "source": [
    "1. Hotel vs number of guests\n",
    "- there seems to be higher number of guest in City Hotel (53 048 @ 62%) company to Resort Hotel (33 512 @ 38%) with a ratio difference of 24%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d94203b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BB           67278\n",
       "SC            9387\n",
       "HB            9047\n",
       "Undefined      488\n",
       "FB             360\n",
       "Name: meal, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.meal.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffe9308",
   "metadata": {},
   "source": [
    "2. Type of Meals Ordered (proportion)\n",
    "- Majority of guests which represent 77.7% (67 728) of population order BB as their meals while some small percentage of guest prder SC and HB respectively at 10.8% and 10.5% of total population. \n",
    "- There is a very little people who order FB as their type of meals while some of the guest, we cannot define their type of meals (488 guest in total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bb860c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Online TA        51295\n",
       "Offline TA/TO    13822\n",
       "Direct           11594\n",
       "Groups            4917\n",
       "Corporate         4020\n",
       "Complementary      686\n",
       "Aviation           226\n",
       "Name: market_segment, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.market_segment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b29103",
   "metadata": {},
   "source": [
    "3. Market Segment\n",
    "- Half of the guest market segment comes from Online TA (59.3%) while Offline TA/TO and Direct represent 16% and 13.4% of market segment respectively\n",
    "- there is small population of market segment from Groups and Corporate\n",
    "- the remainder of market segment is made up of people from Aviation and Complementary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "897bfc2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TA/TO        68731\n",
       "Direct       12761\n",
       "Corporate     4886\n",
       "GDS            181\n",
       "Undefined        1\n",
       "Name: distribution_channel, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.distribution_channel.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f00111",
   "metadata": {},
   "source": [
    "4. Distribution Channel\n",
    "- 68 731 or 79.4% of the Distribution Channel comes from TA/TO while there is small percentage of it comes from Direct (14.7%) and Corporate (5.64%)\n",
    "-the rest is made up of small minority from GDS and Undefined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3e286c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of repeated guests =4.945344291071571%\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of repeated guests ={}%'.format(len(df_not_c[df_not_c['is_repeated_guest']==1])*100/len(df_not_c)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b076cb25",
   "metadata": {},
   "source": [
    "5. Repeated guest\n",
    "-4.95% of whole sample population is repeated guest which means there is a very small segment of customers who become repeated guest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aea3a7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    41406\n",
       "D    12113\n",
       "E     4357\n",
       "F     1960\n",
       "G     1302\n",
       "C      617\n",
       "B      553\n",
       "H      353\n",
       "L        4\n",
       "Name: reserved_room_type, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_c.reserved_room_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d85356b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    31926\n",
       "D    16835\n",
       "E     5434\n",
       "F     2715\n",
       "C     1746\n",
       "G     1728\n",
       "B     1312\n",
       "H      453\n",
       "I      342\n",
       "K      174\n",
       "Name: assigned_room_type, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_c.assigned_room_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "af9c7a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of bookings where assigned room type is not what was reserved room type= 19.364876725444827 %\n"
     ]
    }
   ],
   "source": [
    "print('Proportion of bookings where assigned room type is not what was reserved room type=', len(df_not_c[df_not_c['reserved_room_type']!=df_not_c['assigned_room_type']])*100/len(df_not_c),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2a9589",
   "metadata": {},
   "source": [
    "6. Reserved room type\n",
    "-41 406 from 62 665 or 66% guest reserved Room Type A\n",
    "-some subsequent segment of guest reserved Room Type D which made up of 19% of guest\n",
    "- there is a very small percentage of guest who reserved other room types\n",
    "- However, there is quite a chunck of guest who reserved Room Type A who are assigned to another room type which totals up to 9480 guest\n",
    "- a lot of guest are assigned to Room Type D and Room Type D instead of the reserved Room Type A\n",
    "-Proportion of bookings where assigned room type is not what was reserved room type= 19.36 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8962f00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transient          71317\n",
       "Transient-Party    11575\n",
       "Contract            3134\n",
       "Group                534\n",
       "Name: customer_type, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.customer_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62405c91",
   "metadata": {},
   "source": [
    "7. Customer Type\n",
    "-majority of Customer (82.4%) are Transient, followed by small representation from Transient-Party, Contract & Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "30b8842b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>No.of Guests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRT</td>\n",
       "      <td>17542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBR</td>\n",
       "      <td>8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>7071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESP</td>\n",
       "      <td>5372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEU</td>\n",
       "      <td>4322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>SYC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>MDG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>SMR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>FRO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  No.of Guests\n",
       "0       PRT         17542\n",
       "1       GBR          8428\n",
       "2       FRA          7071\n",
       "3       ESP          5372\n",
       "4       DEU          4322\n",
       "..      ...           ...\n",
       "160     ZMB             1\n",
       "161     SYC             1\n",
       "162     MDG             1\n",
       "163     SMR             1\n",
       "164     FRO             1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country = df_not_c['country'].value_counts().reset_index()\n",
    "df_country.columns = ['Country','No.of Guests']\n",
    "df_country"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85912990",
   "metadata": {},
   "source": [
    "8. country\n",
    "- PRT has highest number of guest (17542) followed by GBR (8428) and FRA (7071). It is also worth mentioning ESP and due made up quite a chunck representation of 5372 and 4322 respectively\n",
    "- MDG, SLE, NAM,FRO & SMR has smallest number of guest out of 164 country in which there is only one (1) guest from the respective country each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1d719f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_resort = df_not_c[(df_not_c['hotel'] == 'Resort Hotel')]\n",
    "df_city = df_not_c[(df_not_c['hotel'] == 'City Hotel')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "33a2d442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>resort price</th>\n",
       "      <th>city price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>48.852694</td>\n",
       "      <td>83.729687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53.977059</td>\n",
       "      <td>88.324398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>57.512456</td>\n",
       "      <td>91.334430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>77.219541</td>\n",
       "      <td>114.368336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>77.818102</td>\n",
       "      <td>124.837552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>109.200465</td>\n",
       "      <td>120.697766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>151.297719</td>\n",
       "      <td>120.346486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>182.256818</td>\n",
       "      <td>123.023174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>98.744746</td>\n",
       "      <td>116.957015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>62.830638</td>\n",
       "      <td>105.594411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>49.162655</td>\n",
       "      <td>88.276587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>64.223176</td>\n",
       "      <td>90.924499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  resort price  city price\n",
       "0       1     48.852694   83.729687\n",
       "1       2     53.977059   88.324398\n",
       "2       3     57.512456   91.334430\n",
       "3       4     77.219541  114.368336\n",
       "4       5     77.818102  124.837552\n",
       "5       6    109.200465  120.697766\n",
       "6       7    151.297719  120.346486\n",
       "7       8    182.256818  123.023174\n",
       "8       9     98.744746  116.957015\n",
       "9      10     62.830638  105.594411\n",
       "10     11     49.162655   88.276587\n",
       "11     12     64.223176   90.924499"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resort = df_resort.groupby(['arrival_date_month'])['adr'].mean().reset_index()\n",
    "city=df_city.groupby(['arrival_date_month'])['adr'].mean().reset_index()\n",
    "\n",
    "combined=resort.merge(city, on = 'arrival_date_month')\n",
    "combined.columns = ['month', 'resort price', 'city price']\n",
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "986a381b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=resort price<br>month=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "resort price",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "resort price",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9,
          10,
          11,
          12
         ],
         "xaxis": "x",
         "y": [
          48.852694427434166,
          53.97705852926463,
          57.51245556594949,
          77.21954141319607,
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          109.20046475195824,
          151.29771935699932,
          182.25681774656877,
          98.74474594594595,
          62.83063800277393,
          49.162655334114895,
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         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "variable=city price<br>month=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "city price",
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    "9. Hotel prices by months\n",
    "-relatively speaking, City Price is relatively higher than Resort Price through out the whole year with exception in Month of 7 & 8\n",
    "-from the graph plot, we can see the price of Resorts Price starts to creeps up from Month 5 and exceed City Price on Months 7. However, price correction starts to happen during Month 9 before it return slowly return to baseline "
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   "source": [
    "10. Total number of guest per months\n",
    "- generally speaking, there are alwasys higher number of guest in City Hotel company to Resort Hotel\n",
    "- However, we can see there is spike in demand for both respective hotel starting from Month 6 before it reach its peak during Month 8\n",
    "- Month 9 is when we start to see decline in number of guest again"
   ]
  },
  {
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   "id": "a552cbd3",
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    {
     "data": {
      "text/plain": [
       "No Deposit    62529\n",
       "Refundable       81\n",
       "Non Refund       55\n",
       "Name: deposit_type, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_c.deposit_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dce09ff",
   "metadata": {},
   "source": [
    "11. Deposit Type\n",
    "- there is small cases of refundable deposit (81 cases) while there is 55 non-refund cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063a50d7",
   "metadata": {},
   "source": [
    "# Q2. Suggest the use cases suitable here from the perspective of the hotel owner?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f1ae96",
   "metadata": {},
   "source": [
    "- the hotel needs to do adequte preparation especially for peak season starting fro Month 6- Month 8\n",
    "\n",
    "- if the hotel management is concerned about the spike in demand will result in full capacity operation, there is some mitigation measures that can be taken in order to dissipate some of the demand to avoid oveflowing\n",
    "\n",
    "- for example, the hotel owner can provide a more attractive discount and deals during the non-peak season which based on our data analysis, usually happen at the beginning and the end of the year to attract more guest during off-peak season\n",
    "\n",
    "- this will allow for a more uniform guest distribution through out the year\n",
    "- at the same time, it also allows the hotel owner to have a steady stream of sales and incomes in which it will results in better cash flow management\n",
    "\n",
    "- correction of cash flow will allow the hotel owner to do business expansion or investment that bring significant return of investment to the hotel owner\n",
    "\n",
    "- it also looks good on account book of the business owner, in case there is a need for audit of the Hotel Owner's company\n",
    "\n",
    "- it is also easier to plan for tax relief program with a steady cash flow and steady stream of customer through out the whole year which will prevent the hotel owner to have deficit in certain months\n",
    "\n",
    "-the hotel owner can further improve the sales of the hotel by doing a custom and specific campaign to attract guest from country with the least amount of guest such as MDG, SLE, NAM, FRO & SMR\n",
    "\n",
    "-the hotel can also give incentive to country with highest number of guest such as PRT, GBR, FRA, ESP & DEU in order to retain customer and build a healty networking\n",
    "\n",
    "-the hotel owner also need to cater more to Transient and Transient-Party Customer as they made up of the majority of the market for the total number of guest\n",
    "\n",
    "- there is a very small cases of refund and non refund. Eventhough it doesnt seems like major issue, it's good to blast a survey form to this customer to determine exactly the reason for refund and non refund in order to improve more on the overall customer experience and avoid future issue when the hotel owner plan to expand\n",
    "\n",
    "-there is a lot of issue in which the assigned room is not the same as the reserved room. this problem need to be issued properly to avoid the hotel to be flooded from bad review and to uphold the reputation of the hotel. we need to determine what exactly causes the mismatch in the assigned and reserved room type and address the issue accordingly.\n",
    "\n",
    "-if the mismatch is due to limited capacity, we need to tweak the booking system to only accept booking up until certain level. if the mismatch is due to peak seasos, the hotel owner can dissipate the demand by offering great deals during non-peak season\n",
    "\n",
    "- there is only 4.95% repeated guest. the hotel owner need to study further what exactly cause this figure? is the price too high for the guest to afford repeated time? or does the guest find the experience at the hotel unpleasant to the point that they dont feel like coming again? or is the location made it higly non-accesible for the guest to come frequently?\n",
    "\n",
    "-Majority of distribution channel come from TA/TO. the hotel owner needs to pay extra attention to this segment as they made up of 79.4% of total market segment which means they contribute the highest revenue to the hotel. \n",
    "\n",
    "- Majority of market segment, more than half of it (59.3%) are made of Online TA. the hotel owner needs to make sure that the system are running smoothly and provide good booking experience to this group\n",
    "\n",
    "-majority of meals ordered by customer is BB. the owner can collet data on customer satisfaction in order to determine what exactly make BB such a successful meal type which allows it to thrive at 77.7& of the whole proportion. and with enough data, maybe we can take the stratergy and features from BB and implement it towards other meals type in order to provide a more  meaningful diversity of choices that are pleasant to the guest\n",
    "\n",
    "- a lot of the customers prefer city hotel rather than resort hotel. this need more data collection in order to determine exactly why the guest prefer this one from another. From there, we can apply the stratergy of success of City Hotel to boost the sales for Resort Hotel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986099a9",
   "metadata": {},
   "source": [
    "# Q3. Suggest the use cases suitable here from the perspective of the guests?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c09015",
   "metadata": {},
   "source": [
    "-there is higher number of guest in City Hotel. depending on the guest, we can suggest different choices based on customer preference. \n",
    "\n",
    "for example, if the customer is the type that dislike crowded places and prefer the view of nature while at the same time want to have a vacation with minimall risk of getting Covid, we can suggest Resort Hotel that is proven to has smaller number of guest\n",
    "\n",
    "however, some guests prefer the view of the city and to get to know new person and met new acquantance, then city hotel is the more suitable choices for them\n",
    "\n",
    "a lot of guest ordered BB meals compare to other type of meals. it's good for the guest to select the same meals to explore exactly what makes BB meals so popular compare to another meal and for them to have immersive experience of the hotel\n",
    "\n",
    "as majority of market segment is from Online TA, it is safe to say that the other guest to also do the same as it is more convenient and easier to do.\n",
    "\n",
    "majority of distribution channel is made up of TA/TO. this means that booking the hotel through Travel Agency (TA) or Travel Office is better as they may be able to provide better deals. Besides, you will have someone to consult and recommend to you the best place and the best meal as they are well versed with the travel destination\n",
    "\n",
    "there is a very small number of repeated guest for this hotel. its better for the guest or the customer to make extensive research about the hotel to find out exactly the reason why there is such a low number of repeated guest. is it because the price is too high for it to be repeatable, is the service bad, or does it locate it remote location, making it not easily aceesible? it's good idea to look up review of the hotel and travel destination online to prepare for worst cases scenario\n",
    "\n",
    "there is a lot of cases in which customer who reserved room type A were assigned to another room type. to avoid this, i think it is better for us to book in advance if we still want Room Type A. for the peace of mind, it is also advisable for us as guest to book other room type that are equally or more comfortable at higher price point to ensure peace of mind and smooth vacation\n",
    "\n",
    "there is very small number of request for deposit refund. and it also seems that majority of customer booking with no deposit. i think its safe to say that booking with no deposit is accetable. however, if there is certain kind of hotel room or hotel view that the guest prefer, i think it's better if we put down some kind of deposit as the hotel management will prioritise these hotel rooms to the one who has reservation and already paid down their deposit\n",
    "\n",
    "there is a lot of transient customer which a lot of them dont stay at the hotel for long duration. it is good to plan the vacation propperly with solid itenary so that we dont prolong our stay at the hotel which will cost us a lot more money\n",
    "\n",
    "there is a lot of customer from PRT, GBR, FRA, ESP and DEU. if you as a guest want to avoid overcrowding, i think its best to suggest that to book the hotel during off-season in which those countries are not on holiday. However, it is a different cases if the guest are from those respective countries.\n",
    "\n",
    "there is spike of hotel price, especially for the resort hotel from month 6-8. if you want to save more money, i think its best to book the hotel during the off-peak season as there is significant difference in the price, provided that the guest are available during that period. however,there is no much variation with hotel in the City. So it is safe to say that you can book the City hotel whenever throughout the whole year as there is very small difference in price.\n",
    "\n",
    "busy month for both city hotel and resort hotel are starting from Month 6-Month 8. if you dont like crowded place, i would suggest to book the hotel during the off-peak season either earlier of the year or at the end of the year \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a3afc9",
   "metadata": {},
   "source": [
    "# Q4. Develop a model to predict bookings cancellation and explain the model as well as it’s output."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfd9fb1",
   "metadata": {},
   "source": [
    "Random Forest Classifier has accuracy of 94%, with weighted precision and recall of 94% which is great."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b0b1f0",
   "metadata": {},
   "source": [
    "Random Forests tend to have high accuracy prediction (challenge C2) and can handle large numbers of features (C1) due to the embedded feature selection in the model generation process. Note that when the number of features is large, it is preferable to use a higher number of regression trees."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed568b09",
   "metadata": {},
   "source": [
    "a good accuracy score for random forest is 92.49 %. \n",
    "our model has Accuracy of 94.00 %. The random forest trained on the single year of data was able to achieve an average absolute error of 4.3 degrees representing an accuracy of 94.00% on the expanded test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f974a6a0",
   "metadata": {},
   "source": [
    "How to improve the model?\n",
    "\n",
    "1.Use more (high-quality) data and feature engineering\n",
    "2.Tune the hyperparameters of the algorithm\n",
    "3.Try different algorithms"
   ]
  }
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