7013SSL: Prediction Analysis on Hotel Booking Cancellation - Management Assignment Help

Assignment Task:

Task:

Introduction and Overview

This report focusses on predicting the booking cancellation of 2 hotels based on their historical data. The master dataset used for this report consists of more than 111,000 records of customer bookings combined for both the hotels from July 2015 to August 2017. One of the hotels is a plush Resort whereas the other one is a City based hotel (Antonio, de Almeida and Nunes, 2019). Since these are hotels with actual data relevant to real people, sensitive information such as the identification details of the Hotels and the customers is omitted as per the GDPR guidelines (Greengard, 2018). The type of database in this case is a ‘Structured’ data as it is extracted from the Hotel’s SQL databases and is highly organised into tables and columns (Gandomi and Haider 2015). The booking system of these hotels allow 3 types of bookings. They include direct booking, corporate booking, and booking by a Travel agent (Antonio, de Almeida and Nunes, 2019). As a part of marketing and expansion plan, Hotel operators have started operating websites as well as tie ups with OTAs i.e. Online Travel Agents. However, it is clear from various reports that this approach did not help increase their profits/revenues. An example of this is the website development investment from the Four Seasons hotel who invested 18 million USD for a marketing and booking, but it increased only 2% of revenue in a duration of 5 years (Liu and Zhang 2014). Also, the revenue from online bookings contributed only 12% of the total revenue (Fox 2020). The scope of the report is to summarise and understand the different factors for predicting the cancellation of bookings for the hotels. The report will first carry out Descriptive statistics for summarising the variables followed by Logistic regression to find probability of booking cancellation in the future.

2. Marketing Data summarised This section summarises the important variables involved, their types, and significance with descriptive statistics. As mentioned in Appendix 1, there are more than 30 variables as part of the master data. Out of those, only relevant variables were chosen as per studies to see their impact. a. is_cancelled This variable shows the number of bookings which were cancelled and not cancelled after being placed. This is a Boolean type of a variable

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