Chapter 1 - Hotel Booking Deman Datasets Preparation.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import gc\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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     "execution_count": 2,
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   ],
   "source": [
    "### H1 Resort hotel\n",
    "### H2 City hotel\n",
    "dataset = pd.read_csv(\"D:\\Book\\H2.csv\")\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill na with 0 for children\n",
    "dataset['Children'].fillna(0,inplace=True)\n",
    "\n",
    "# create arrival date as ArrivalDate\n",
    "monthmap = {'January':1, 'February':2, 'March':3, 'April':4, 'May':5, 'June':6,'July':7, 'August':8, 'September':9, 'October':10, 'November':11, 'December':12}\n",
    "dataset['ArrivalDateMonthNumber'] = dataset['ArrivalDateMonth'].map(monthmap)\n",
    "dataset['ArrivalDate'] = pd.to_datetime(dataset['ArrivalDateDayOfMonth'].astype(str) + '-' + dataset['ArrivalDateMonth'].astype(str) + '-'+ dataset['ArrivalDateYear'].astype(str))\n",
    "\n",
    "#create booking date\n",
    "dataset['BookingDate'] = dataset['ArrivalDate'] - pd.to_timedelta(dataset['LeadTime'], unit='d')\n",
    "\n",
    "#create stay days\n",
    "dataset['StaysInDays'] = dataset['StaysInWeekNights'] + dataset['StaysInWeekendNights']\n",
    "\n",
    "#Change format and make it pandas date\n",
    "dataset['ReservationStatusDate']=pd.to_datetime(dataset['ReservationStatusDate'])\n",
    "#dataset['StaysInDaysCalc'] = (dataset['ReservationStatusDate'] - dataset['ArrivalDate']).dt.days\n",
    "\n",
    "#Remove no-show, as we don't know for how many nights it was reserved. Size of the data is also very small.\n",
    "#Remove cases where there are no guests. both adults and children is 0.\n",
    "#We are only concerned with Transient bookings, as this is the major source of revenue for hotels\n",
    "#For ADR, we will restrict with values above 5 percentile and below 99.99 percentile. rest of the data looks like outlier. As we do not have means to get clarification for data, we will remove data beyond this. \n",
    "\n",
    "dataset=dataset[(dataset['ReservationStatus']!='No-Show') & ((dataset['Adults']>0) | (dataset['Children']>0)) & (dataset['CustomerType']=='Transient') & (dataset['ADR']<=np.percentile(dataset['ADR'],99.99)) & (dataset['ADR']>np.percentile(dataset['ADR'],5))]\n",
    "dataset.reset_index(inplace=True)\n",
    "\n",
    "#If a transaction reflects multiple nights stay, we will add the nights for the following arrival days and add number of days for lead time as well.\n",
    "#So 2 new columns AdjustedArrivalDate and AdjustedLeadTime will be created.\n",
    "\n",
    "dataset = dataset.loc[dataset.index.repeat(dataset['StaysInDays'])].assign(CarryForwardForFutureDays=1)\n",
    "dataset['KeepIndex'] = dataset.index.values\n",
    "dataset['AdjustBy'] = dataset.groupby(['KeepIndex'])['CarryForwardForFutureDays'].cumsum()-1\n",
    "dataset['AdjustedArrivalDate'] = dataset['ArrivalDate'] + pd.to_timedelta(dataset['AdjustBy'], unit='d')\n",
    "dataset['AdjustedLeadTime'] = dataset['LeadTime'] + dataset['AdjustBy']\n",
    "dataset.reset_index(inplace=True)\n",
    "dataset=dataset[['IsCanceled', 'AdjustedLeadTime', 'LeadTime', 'ArrivalDateYear','ArrivalDateMonth', 'ArrivalDateWeekNumber', 'ArrivalDateDayOfMonth','StaysInWeekendNights', 'StaysInWeekNights', 'Adults', 'Children','Babies', 'Meal', 'Country', 'MarketSegment', 'DistributionChannel','IsRepeatedGuest', 'PreviousCancellations','PreviousBookingsNotCanceled', 'ReservedRoomType', 'AssignedRoomType','BookingChanges', 'DepositType', 'Agent', 'Company','DaysInWaitingList', 'CustomerType', 'ADR', 'RequiredCarParkingSpaces','TotalOfSpecialRequests', 'ReservationStatus', 'ReservationStatusDate','ArrivalDateMonthNumber', 'ArrivalDate', 'BookingDate', 'StaysInDays','AdjustedArrivalDate']]\n",
    "\n",
    "## We will calculate how many rooms were booked using below method.\n",
    "\n",
    "# keep 0 as placeholder in new column\n",
    "dataset['NumberOfRooms'] = 0\n",
    "\n",
    "## One room can accomodate 2 adults and 2 kid in a hotel.\n",
    "#If a transaction has 2 adult and 2 kid, it will be considered as 1 room. If a transaction has 1 adult and 2 kid, 2 adult and 1 kid, or 1 adult and 1 kid, it will be considered as 1 room.\n",
    "# Room =1\n",
    "# If adults less than or equal to 2 and 2 children. Or no adult and less than or equal to 2 children.\n",
    "dataset['NumberOfRooms'][((dataset['Adults']<=2) & (dataset['Children']<=2))] = 1\n",
    "\n",
    "## For any other transaction, we will follow below rules.\n",
    "#divide by 2 and round up, if no kids or no adults\n",
    "dataset['NumberOfRooms'][(dataset['Adults']>2) & (dataset['Children']==0)] = np.round(dataset['Adults']/2)\n",
    "\n",
    "#3 adult with <=2 kid, 2 room\n",
    "dataset['NumberOfRooms'][((dataset['Adults']==3) & (dataset['Children']<=2))] = 2\n",
    "\n",
    "#<=2 adult with 3 kid, 2 room\n",
    "dataset['NumberOfRooms'][((dataset['Adults']<=2) & (dataset['Children']==3))] = 2\n",
    "\n",
    "############################"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "\n",
    "###############create dataframe in desired format for modelling\n",
    "def create_desired_df(dataset2_aggregation_df):\n",
    "    sdate = dataset2_aggregation_df['AdjustedArrivalDate'].min()\n",
    "    edate = dataset2_aggregation_df['AdjustedArrivalDate'].max()\n",
    "    date_range = list(pd.date_range(sdate,edate,freq='d'))\n",
    "\n",
    "    desired_df = pd.DataFrame({'AdjustedLeadTime':list(i for i in range(dataset2_aggregation_df['AdjustedLeadTime'].max()+1,-1,-1))})\n",
    "    desired_df = desired_df.loc[np.tile(desired_df.index,len(date_range))].assign(AdjustedArrivalDate=np.repeat(date_range,len(desired_df)))\n",
    "\n",
    "    desired_df.reset_index(inplace=True,drop=True)\n",
    "    return desired_df\n",
    "\n",
    "def create_decode_dict(desired_df,dataset2_aggregation_df):\n",
    "    desired_df=desired_df.merge(dataset2_aggregation_df,how='left')\n",
    "    desired_df.fillna(0,inplace=True)\n",
    "\n",
    "    reverse_list=list(i for i in range(dataset2_aggregation_df['AdjustedLeadTime'].max()+1,-1,-1))\n",
    "    correct_list=list(i for i in range(0,dataset2_aggregation_df['AdjustedLeadTime'].max()+2,1))\n",
    "\n",
    "    recode_dict={}\n",
    "    for i,j in zip(reverse_list,correct_list):\n",
    "        recode_dict[i]=j\n",
    "    return recode_dict\n",
    "\n",
    "def create_cumulative(desired_df,dataset2_aggregation_df,NumberOfRooms,recode_dict):\n",
    "    ## merge both in single dataframe to get booking and cancellation data at arrival date level for lead time\n",
    "    desired_df=desired_df.merge(dataset2_aggregation_df,how='left')\n",
    "    desired_df.fillna(0,inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "    desired_df['AdjustedLeadTime'] = desired_df['AdjustedLeadTime'].map(recode_dict)\n",
    "\n",
    "    desired_df2=desired_df.groupby(['AdjustedArrivalDate', 'AdjustedLeadTime']).sum() \\\n",
    "      .groupby(level=0).cumsum().reset_index()\n",
    "    desired_df['Cumulative'+NumberOfRooms] = desired_df2[NumberOfRooms]\n",
    "    desired_df['Cumulative'+NumberOfRooms].fillna(0,inplace=True)\n",
    "    \n",
    "    desired_df['AdjustedLeadTime'] = desired_df['AdjustedLeadTime'].map(recode_dict)\n",
    "\n",
    "    return desired_df\n",
    "\n",
    "\n",
    "def create_data(dataset,keep_columns,calc_column):\n",
    "    dataset2=dataset[keep_columns]\n",
    "\n",
    "    ## get reservation summed by arrival date, at lead time level\n",
    "    dataset2_aggregation=dataset2[['AdjustedArrivalDate','AdjustedLeadTime',calc_column]].groupby(['AdjustedArrivalDate','AdjustedLeadTime']).sum()\n",
    "    dataset2_aggregation_df = pd.DataFrame({'AdjustedArrivalDate':dataset2_aggregation.index.get_level_values(0).to_list(),\n",
    "    'AdjustedLeadTime':dataset2_aggregation.index.get_level_values(1).to_list(),\n",
    "    calc_column:dataset2_aggregation.values.ravel()})\n",
    "\n",
    "    ## get cancellation aggregation by arrival date, at ReservationStatusDate level by aggregating cancellation at the lead time level when they are canelled\n",
    "    dataset2_cancel = dataset2[dataset2['IsCanceled']==1]\n",
    "    dataset2_cancel.reset_index(inplace=True,drop=True)\n",
    "\n",
    "    dataset2_cancel['AdjustedLeadTime'] = (dataset2_cancel['AdjustedArrivalDate']-dataset2_cancel['ReservationStatusDate']).dt.days\n",
    "    dataset2_cancel_aggregation = dataset2_cancel[['AdjustedArrivalDate','AdjustedLeadTime',calc_column]].groupby(['AdjustedArrivalDate','AdjustedLeadTime']).sum()\n",
    "    dataset2_cancel_aggregation_df = pd.DataFrame({'AdjustedArrivalDate':dataset2_cancel_aggregation.index.get_level_values(0).to_list(),\n",
    "    'AdjustedLeadTime':dataset2_cancel_aggregation.index.get_level_values(1).to_list(),\n",
    "    calc_column+'_Substract':dataset2_cancel_aggregation.values.ravel()})\n",
    "\n",
    "    desired_df1 = create_desired_df(dataset2_aggregation_df)\n",
    "    recode_dict = create_decode_dict(desired_df1,dataset2_aggregation_df)\n",
    "    desired_df2 = create_desired_df(dataset2_aggregation_df)\n",
    "\n",
    "    desired_df1 = create_cumulative(desired_df1,dataset2_aggregation_df,calc_column,recode_dict)\n",
    "    desired_df2 = create_cumulative(desired_df2,dataset2_cancel_aggregation_df,calc_column+'_Substract',recode_dict)\n",
    "\n",
    "    desired_df=desired_df1.merge(desired_df2,how='left')\n",
    "    #     desired_df.fillna(0,inplace=True)\n",
    "\n",
    "    desired_df['Cumulative'+calc_column+'Net'] = desired_df['Cumulative'+calc_column] - desired_df['Cumulative'+calc_column+'_Substract']\n",
    "\n",
    "    final_output = desired_df[desired_df['AdjustedLeadTime']==0][['AdjustedArrivalDate','AdjustedLeadTime','Cumulative'+calc_column+'Net']]\n",
    "    final_output.rename(columns={'Cumulative'+calc_column+'Net':'TotalRooms'},inplace=True)\n",
    "\n",
    "    desired_df=desired_df.merge(final_output,how='left')\n",
    "    desired_df['TotalRooms'].fillna(method='bfill',inplace=True)\n",
    "    return desired_df\n",
    "\n",
    "#########################\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "###number of rooms\n",
    "keep_columns=['AdjustedArrivalDate','NumberOfRooms','BookingDate','AdjustedLeadTime','ReservationStatusDate','IsCanceled']\n",
    "desired_df = create_data(dataset,keep_columns,calc_column='NumberOfRooms')\n",
    "#keep data for less than or equal to 100 Adjusted lead time\n",
    "desired_df = desired_df[(desired_df['AdjustedLeadTime'] <= 100)]\n",
    "desired_df.reset_index(inplace=True,drop=True)\n",
    "\n",
    "\n",
    "dataset['Revenue'] = dataset['ADR'] * dataset['NumberOfRooms']\n",
    "keep_columns=['AdjustedArrivalDate','Revenue','BookingDate','AdjustedLeadTime','ReservationStatusDate','IsCanceled']\n",
    "desired_df2 = create_data(dataset,keep_columns,calc_column='Revenue')\n",
    "desired_df2.rename(columns={'TotalRooms':'TotalRevenue'},inplace=True)\n",
    "desired_df2 = desired_df2[(desired_df2['AdjustedLeadTime'] <= 100)]\n",
    "desired_df2.reset_index(inplace=True,drop=True)\n",
    "del desired_df2['TotalRevenue']\n",
    "\n",
    "desired_df = desired_df.merge(desired_df2,how='left')\n",
    "\n",
    "##create seasonality columns\n",
    "\n",
    "#day of the week\n",
    "desired_df['DayOfWeek'] = desired_df['AdjustedArrivalDate'].dt.day_name()\n",
    "\n",
    "#weekend\n",
    "desired_df['Weekend'] = np.where(desired_df['DayOfWeek'].isin(['Sunday', 'Saturday']), 1,0)\n",
    "\n",
    "#\n",
    "daymap = {'Monday':1,'Tuesday':2,'Wednesday':3, 'Thursday':4, 'Friday':5, 'Saturday':6, 'Sunday':7}\n",
    "desired_df['DayOfWeek'] = desired_df['DayOfWeek'].map(daymap)\n",
    "\n",
    "#week of the year\n",
    "desired_df['WeekOfYear'] = desired_df['AdjustedArrivalDate'].dt.week\n",
    "\n",
    "#day of the month\n",
    "desired_df['DayOfMonth'] = desired_df['AdjustedArrivalDate'].dt.day\n",
    "\n",
    "#week of the month\n",
    "desired_df['WeekOfMonth'] = pd.to_numeric(desired_df['DayOfMonth']/7)\n",
    "desired_df['WeekOfMonth'] = desired_df['WeekOfMonth'].apply(lambda x: math.ceil(x))\n",
    "\n",
    "#month of the year\n",
    "desired_df['MonthOfYear'] = desired_df['AdjustedArrivalDate'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "desired_df.to_csv(\"D:\\Book\\H2_PredictingOccupancy_100daysPrior.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AdjustedLeadTime</th>\n",
       "      <th>AdjustedArrivalDate</th>\n",
       "      <th>NumberOfRooms</th>\n",
       "      <th>CumulativeNumberOfRooms</th>\n",
       "      <th>NumberOfRooms_Substract</th>\n",
       "      <th>CumulativeNumberOfRooms_Substract</th>\n",
       "      <th>CumulativeNumberOfRoomsNet</th>\n",
       "      <th>TotalRooms</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>CumulativeRevenue</th>\n",
       "      <th>Revenue_Substract</th>\n",
       "      <th>CumulativeRevenue_Substract</th>\n",
       "      <th>CumulativeRevenueNet</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>WeekOfYear</th>\n",
       "      <th>DayOfMonth</th>\n",
       "      <th>WeekOfMonth</th>\n",
       "      <th>MonthOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7180.0</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "   AdjustedLeadTime AdjustedArrivalDate  NumberOfRooms  \\\n",
       "0               100          2015-07-01            0.0   \n",
       "1                99          2015-07-01            0.0   \n",
       "2                98          2015-07-01            0.0   \n",
       "3                97          2015-07-01            0.0   \n",
       "4                96          2015-07-01            0.0   \n",
       "\n",
       "   CumulativeNumberOfRooms  NumberOfRooms_Substract  \\\n",
       "0                     74.0                      0.0   \n",
       "1                     74.0                      0.0   \n",
       "2                     74.0                      0.0   \n",
       "3                     74.0                      0.0   \n",
       "4                     74.0                      0.0   \n",
       "\n",
       "   CumulativeNumberOfRooms_Substract  CumulativeNumberOfRoomsNet  TotalRooms  \\\n",
       "0                                0.0                        74.0        65.0   \n",
       "1                                0.0                        74.0        65.0   \n",
       "2                                0.0                        74.0        65.0   \n",
       "3                                0.0                        74.0        65.0   \n",
       "4                                0.0                        74.0        65.0   \n",
       "\n",
       "   Revenue  CumulativeRevenue  Revenue_Substract  CumulativeRevenue_Substract  \\\n",
       "0      0.0             7180.0                0.0                          0.0   \n",
       "1      0.0             7180.0                0.0                          0.0   \n",
       "2      0.0             7180.0                0.0                          0.0   \n",
       "3      0.0             7180.0                0.0                          0.0   \n",
       "4      0.0             7180.0                0.0                          0.0   \n",
       "\n",
       "   CumulativeRevenueNet  DayOfWeek  Weekend  WeekOfYear  DayOfMonth  \\\n",
       "0                7180.0          3        0          27           1   \n",
       "1                7180.0          3        0          27           1   \n",
       "2                7180.0          3        0          27           1   \n",
       "3                7180.0          3        0          27           1   \n",
       "4                7180.0          3        0          27           1   \n",
       "\n",
       "   WeekOfMonth  MonthOfYear  \n",
       "0            1            7  \n",
       "1            1            7  \n",
       "2            1            7  \n",
       "3            1            7  \n",
       "4            1            7  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "desired_df.head()"
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