Hotel Booking Prediction Project Description
The Hotel Booking Prediction project will explain the workflow for assessing someone, whether to cancel the booking or not. This assessment is based on a machine learning algorithm, which will provide predictions to the customer who makes a booking, then from the data provided it will be predicted about the cancellation. This project also contains various analyzes obtained through EDA and provides various insights on hotels to develop their business more effectively.
Packages
- Pandas
- Numpy
- Seaborn
- Matplotlib
Guideline Project
- Preparing Data for Analysis and Modeling
- Analysing Home Country Guest with Spatial Analysis
- Analysing Average Price for a Room per Night
- Analysing Price variations per night throughout the year
- Analysing The most busy month
- Analysing How Long do People Stay at the Hotels
- Select Important Features Using Corelation
- Handling Outlier
- Splitting Dataset & Build Model
- Hyper Tuning Parameter
- Confusion Matrix Random Forest
About
Hotel is a business that depends on the number of visitors. In order to get maximum profit is requires a lot of visitors. Hotels also have a certain capacity, so there are limitations in accepting visitors. Because of these problems, the hotel provides a booking system, so that visitors can order services in advance and get services according to the desired day.
Project Link
Youtube Presentation : https://youtu.be/k49YFMUZ0ok
File Presentation : https://www.canva.com/design/DAEpy8CWxzU/Ejvf2fjIHSEYDt06o0-MBA/view?utm_content=DAEpy8CWxzU&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton
LinkedIn Post : https://www.linkedin.com/posts/rikasahriana_hotel-booking-prediction-activity-6851057355434663936-2Nvg
Data Dictionary
hotelHotel (H1 = Resort Hotel or H2 = City Hotel) is_canceled=Value indicating if the booking was canceled (1) or not (0) lead_time= Number of days that elapsed between the entering date of the booking into the PMS and the arrival date arrival_date_year =Year of arrival date arrival_date_month =Month of arrival date arrival_date_week_number Week number of year for arrival date arrival_date_day_of_month=Day of arrival date stays_in_weekend_nights=Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel stays_in_week_nights=Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel adults=Number of adults children=Number of children babies=Number of babies
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)
country=Country of origin. Categories are represented in the ISO 3155–3:2013 format market_segment=Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators” distribution_channel=Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators” is_repeated_guest=Value indicating if the booking name was from a repeated guest (1) or not (0) previous_cancellations=Number of previous bookings that were cancelled by the customer prior to the current booking previous_bookings_not_canceled=Number of previous bookings not cancelled by the customer prior to the current booking reserved_room_type=Code of room type reserved. Code is presented instead of designation for anonymity reasons.
assigned_room_type 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.
booking_changes=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
deposit_type=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.
agent=ID of the travel agency that made the booking company=ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for anonymity reasons days_in_waiting_list=Number of days the booking was in the waiting list before it was confirmed to the customer customer_type
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 adr=Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights required_car_parking_spaces=Number of car parking spaces required by the customer total_of_special_requests=Number of special requests made by the customer (e.g. twin bed or high floor) reservation_status=Reservation last status, assuming 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 reservation_status_date=Date at which the last status was set. This variable can be used in conjunction with the Reservation Status to understand when was the booking canceled or when did the customer checked-out of the hotel
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