BI And Data Analytics

Overview In hotel industry, it is very common that customers cancel their bookings before they check-in or do not show up at the time of their check-in. Both cases are usually shown as cancellation in the hotel’s booking system. Predicting a hotel booking’s likelihood to be cancelled can help the hotel manager to effectively allocate rooms in their booking systems. In this assessment, you are going to predict the hotel booking cancellations using data-driven models. The data related to this assessment can be downloaded from Teams. You need to write an analysis report to discuss how do you complete the tasks and go into sufficient depth to demonstrate knowledge and critical understanding of the relevant processes involved. 100% of available marks are through the completion of the written report. Report Guidance Your report must conform to the below structure and include the required content as described. You must supply a written report containing three distinct sections that provide a full and reflective account of the processes undertaken.


Section I: Data Loading and Preparation (15%)

As a first step, you need to download the datasets from Teams. There are two datasets: hotel_bookings_01.csv and hotel_bookings_02.csv. The variables in both datasets are briefly explained as below:

Variable Description ADR Average daily rate Adults Number of adults Agent ID of the travel agency that made the booking. Null is there is no agent. ArrivalDateDayOfMonth Day of the month of the arrival date ArrivalDateMonth Month of arrival date with 12 categories: “January” to “December” ArrivalDateWeekNumber Week number of the arrival date ArrivalDateYear Year of arrival date AssignedRoomType Code for the type of room assigned to the booking. Sometimes the assigned

room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons

Babies Number of babies BookingChanges Number of changes/amendments made to the booking from the moment the

booking was entered on the PMS until the moment of check-in or cancellation Children Number of children Country Country of origin. Categories are represented in the ISO 3155–3:2013 format CustomerType Type of booking, assuming one of four categories: Contract – when the

booking has an allotment or other type of contract associated to it; Group – when the booking is associated to a group; Transient – when the booking is not part of a group or contract, and is not associated to other transient booking; Transient-party – when the booking is transient, but is associated to at least other transient booking

DaysInWaitingList Number of days the booking was in the waiting list before it was confirmed to the customer

DepositType Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit – no deposit was made; Non Refund – a deposit was made in the value of the total stay cost; Refundable – a deposit was made with a value under the total cost of stay.

IsCanceled Value indicating if the booking was canceled (1) or not (0)



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Is Repeated Guest Value indicating if the booking name was from a repeated guest (1) or not (0) LeadTime Number of days that elapsed between the entering date of the booking into the

PMS and the arrival date Meal Type of meal booked. Categories are presented in standard hospitality meal

packages: Undefined/SC – no meal package; BB – Bed & Breakfast; HB – Half board (breakfast and one other meal – usually dinner); FB – Full board (breakfast, lunch and dinner)

PreviousBookingsNotCanceled Number of previous bookings not cancelled by the customer prior to the current booking

PreviousCancellations Number of previous bookings that were cancelled by the customer prior to the current booking

RequiredCardParkingSpaces Number of car parking spaces required by the customer ReservationStatus Reservation last status, in one of three categories: Canceled – booking was

canceled by the customer; Check-Out – customer has checked in but already departed; No-Show – customer did not check-in and did inform the hotel of the reason why

ReservedRoomType Code of room type reserved. Code is presented instead of designation for anonymity reasons

StaysInWeekendNights Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel

StaysInWeekNights Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel

TotalOfSpecialRequests Number of special requests made by the customer (e.g. twin bed or high floor) 1. You firstly merge the two datasets hotel_bookings_01.csv and hotel_bookings_02.csv using R. You need to

provide screenshots of the key steps and report the dimension (i.e., number of rows and number of columns) of the merged dataset. (4%)

2. Do you realise any feature columns in the merged dataset that have missing values? If so, please report these

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