Skip to content

This project aims to uncover the factors behind high cancellation rates in City & Resort Hotels, enabling data-driven strategies to enhance revenue and room utilization.

Notifications You must be signed in to change notification settings

Deepubhatt/Hotel-Cancellation-Rate-Analysis-and-Business-Revenue-Optimization

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Hotel Booking Analysis (Tackling Cancellations Rates)

Dataset Overview

The dataset contains information about bookings in two types of hotels, City and Resort Hotels. Both share the same structure, with 36 variables describing the 40,060 observations of the Resort Hotel and 79,330 observations of the City Hotel. Bookings recorded from July 1, 2015, to August 31, 2017, include successful and canceled bookings.

Problem Statement

Both City & Resort Hotels are experiencing high cancellation rates, leading to reduced revenues and under-utilized rooms affecting the operational efficiency of the Hotels.

Objective

The primary objective of this analysis is to understand the factors contributing to high cancellation rates in both City Hotel and Resort Hotel. By identifying these factors, we can develop targeted strategies to minimize cancellations, increase revenue, and optimize hotel room utilization.

Insights & Possible Solutions

Sharing key insights gained from the analysis, revealing patterns & correlations impacting cancellation rates. Providing actionable solutions and strategies to minimize cancellations and improve bookings and revenue generation.