inn-hotels-bookings

Hotel Booking Cancellation Prediction

Project Overview

INN Hotels Group, a chain of hotels in Portugal, faces a significant issue with booking cancellations. These cancellations not only impact revenue but also increase operational costs, especially with last-minute cancellations. The rise of online booking channels has changed customer behavior, making it more difficult for hotels to predict cancellations. This project aims to develop a machine learning model to predict the likelihood of a booking being canceled, enabling the hotels to formulate effective cancellation policies and mitigate losses.

Objective

The goal of this project is to:

1. Analyze the factors that influence hotel booking cancellations.
2. Build a predictive model to identify bookings that are likely to be canceled.
3. Provide insights to help formulate more profitable cancellation and refund policies.

Data Description

The dataset contains various attributes of customers' booking details. Below is the data dictionary:

Column Name	Description
Booking_ID	Unique identifier of each booking
no_of_adults	Number of adults
no_of_children	Number of children
no_of_weekend_nights	Number of weekend nights booked/stayed (Saturday or Sunday)
no_of_week_nights	Number of weeknights booked/stayed (Monday to Friday)
type_of_meal_plan	Meal plan selected by the customer:
	- Not Selected: No meal plan selected
	- Meal Plan 1: Breakfast
	- Meal Plan 2: Half board (breakfast + one meal)
	- Meal Plan 3: Full board (breakfast, lunch, and dinner)
required_car_parking_space	Customer requested car parking space (0 - No, 1 - Yes)
room_type_reserved	Room type reserved (encoded by the hotel group)
lead_time	Number of days between booking date and arrival date
arrival_year	Year of arrival
arrival_month	Month of arrival
arrival_date	Day of the month for arrival
market_segment_type	Market segment of the booking
repeated_guest	Repeated guest flag (0 - No, 1 - Yes)
no_of_previous_cancellations	Number of previous cancellations by the customer
no_of_previous_bookings_not_canceled	Number of previous bookings not canceled by the customer
avg_price_per_room	Average price per day of the reservation (in euros)
no_of_special_requests	Total number of special requests made by the customer
booking_status	Booking status flag (1 - Canceled, 0 - Not Canceled)

Exploratory Data Analysis (EDA) Questions

1. What are the busiest months in the hotel?

   • Analyze which months see the highest volume of bookings to understand seasonality trends.

2. Which market segment do most guests come from?

   • Examine the market segment breakdown to understand the distribution of guests.

3. What are the differences in room prices across market segments?

   • Analyze how room prices vary by market segment, considering dynamic pricing strategies.

4. What percentage of bookings are canceled?

   • Calculate the overall cancellation rate to assess the extent of the issue.

5. What percentage of repeating guests cancel?

   • Understand how cancellation behavior differs between first-time and repeating guests.

6. Do special requests affect booking cancellations?

   • Investigate whether guests with special requests have a higher likelihood of cancellation.