Hotel Bookings Analysis

Overview

This repository contains an analysis of hotel bookings data. The goal is to identify cancellation trends and provide recommendations for reducing cancellations and increasing revenue.

Dataset

The dataset includes information on hotel bookings such as booking cancellation status, lead time, arrival date, customer type, and more.

Analysis and Insights

1. Cancellation Rates by Hotel Type

• City Hotels: Higher cancellation rate (41.73%) compared to Resort Hotels (27.76%).

2. Monthly Cancellation Trends

• Peak Months: April, June, September, and October have the highest cancellation rates.

3. Customer Type Cancellations

• Transient Customers: Most prone to cancellations (40.75%).
• Group Customers: Lowest cancellation rate (10.23%).

4. Impact of Lead Time on Cancellations

• Higher Lead Times: Associated with higher cancellation densities, particularly around 50-200 days.

5. Special Requests and Cancellations

• No Special Requests: Highest cancellation rate (47.72%).

Recommendations

1. City Hotels: Revise booking policies and enhance customer engagement.
2. Targeted Promotions: Implement during peak cancellation months.
3. Loyalty Programs: Develop for transient customers.
4. Dynamic Pricing: Adjust room rates based on demand and booking trends.
5. Early Bird Discounts: Encourage moderate lead times that reduce cancellations.
6. Special Requests: Promote and efficiently handle special requests to increase commitment.

Files in the Repository

• hotel_bookings_analysis.ipynb: Colab notebook with the analysis.
• Hotel Bookings.csv: Dataset used for the analysis.
• README.md: This file.