Question

Variable 2 hotel 3 is canceled lead time 5 arrival date_year 6 arrival_date_month 7 arrival date week number 8 arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights 10 11 adults 12 children 13 babies meal Description Hotel (H1 - Resort Hotel or H2 = City Hotel) Value indicating if the booking was canceled (1) or not (0) Number of days that elapsed between the entering date of the booking into the PMS and the arrival date Year of arrival date Month of arrival date Week number of year for arrival date Day of arrival date Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel Number of adults Number of children Number of babies 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) Country of origin. Categories are represented in the ISO 3155-3:2013 format Market segment designation. In categories, the term "TA" means "Travel Agents" and "TO" means "Tour Operators" Booking distribution channel. The term "TA" means "Travel Agents" and "TO" means "Tour Operators" Value indicating if the booking name was from a repeated guest (1) or not (0) Number of previous bookings that were cancelled by the customer prior to the current booking Number of previous bookings not cancelled by the customer prior to the current booking 15 country market_segment 16 distribution channel 18 is repeated guest previous cancellations previous_bookings_not_canceled 20 reserved room_type assigned_room_type Code of room type reserved. Code is presented instead of designation for anonymity reasons. 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 (eg, overbooking) or by customer request. Code is presented instead of designation for anonymity reasons. booking changes 23 deposit_type 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 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 i t . . .

Answer 1

Solution:

1. As the variable description file goes, the information given in the variable descriptors is likely related to some hotel business. The business owner(s) is likely interested to understand the nature and behavioural pattern of its visiting customers based on various categories it can possibly identify to collect information on. The information collected is in the form of some data based on which certain business metrics are calculated and used for analyzing the business propects of the future.

A sound business problem can be only effectively analyzed and understood if statistical perspectives are taken into consideration. Business based data and metrics created are utilized to convert the business problem in hand into a quantitative (statistical) problem and various statistical tools and techniques are employed in the analysis to gain fruitful insights into the true nature of the business.

But, for a meaningful analysis that is really insightful, data and information need to be collected on various factors called "variable". Each variable is a certain information, say the number of guests arriving in the hotel on Christmas. Knowing the possible number of visitors in advance may help the hotel to plan accordingly. In the problem in hand, some of the variables may be understood deeply to understand some of the factors that can drive business. For example, date and time are common variables to have a basic arrival sense of customers visiting the hotel. Collecting data on weekly or daily visits also helps us to classify visits at certain time periods. Weekend andd Weekdays stay information is needed to know whether customers visit hotels more on working days or on days with no work.

All the information discussed above are collected on some granularity level for better understanding. Like for example, type of meal has various sub-levels to help understand meal preference. Assigned room type helps the hotel to know the room preference of customers. Variables like distribution channel and marketing modes helps the hotel to know the mode customers prefer more so they can optimize their investment in the most popular channels over time. Basically each variable has a meaning.

Thus, a combination of all the variables in an meaningful manner and utilizing statistical models and methods can help us identifyiing a predictive model which can tell the hotel how to maximize revenue by employing the most optimal mix of the dependent variables concerned on the hotel's profit or revenue.

Last but not the least, the dataset involved here is a population since it contains an entire list of possible data values. A sample can be obtained from the dataset, if certain groups involved in the population can be subsetted out to find certain characteristics and comapre it with the population

2. The data description file being already in place, the dataset can only be explained upto the fact regarding the nature of the variable involved. To say the least, it can be told whether the variables are categorical or numerical, discrete or continuous. They are as :

a. Hotel : Categorical (Nominal, unless you like City Hotels more than resort)

b. is_canceled : Dichotomous (Binary)

c. lead_time : numeric

d: arrival_date_year : Date format

e: arrival_date_month/year/week_number/_day_of_month : Date format

f. stays_in_week_nights/weekend_nights : Numeric

g. adults/children/babies : numeric

h. meal: categorical (nominal : breakfast, lunch, dinner)

i. country : categorical (nominal/ordinal, as per choice)

j. market_segment/distribution_channel : Categorical (nominal sub categories)

k. is_repeated_guest : Indicator (0 or 1)

l. previous_cancellations/previous_bookings_not_cancelled : numeric

m. reserved/assigned_room_type : categorical (nominal)

n. booking_changes : numeric

o. Deposit_type : categorical (can be ordinal)

Rewording of any variable definitions could have been proceeded for but is not done since the original objective behind the dataset is not fully known and just the data description file is given with no actual analysis objective given. A well defined objective can help scrutinize the variables in a more well fashioned manner and manipulate the data and variable definitions to assist the data collection methodology & analysis.