Hotel Reservation Performance & Cancellation Analysis
Executive Summary
Comprehensive exploratory and operational analysis of 36,000+ guest booking transactions to diagnose cancellation drivers, evaluate channel performance, and optimize revenue across room inventory.
1. Business Context
Hospitality management faces high revenue volatility due to late cancellations and variable booking lead times. This project delivers an interactive executive dashboard enabling hotel managers to optimize occupancy and minimize unrecoverable inventory loss.
2. Core Operational Metrics
- Total Bookings: 36,275 records analyzed.
- Cancellation Rate: ~32.8% of total reservations cancelled prior to arrival.
- Lead Time Correlation: Bookings made >90 days in advance exhibit over 2.4x the cancellation rate of bookings made within 14 days.
- Average Daily Rate (ADR): $103.40 across all room types.
3. Key Visualizations Built
- Booking Status Breakdown: Donut chart displaying Confirmed vs Cancelled vs No-Show.
- Channel Contribution: Bar chart evaluating Online Travel Agencies (OTAs) vs Direct vs Corporate.
- Room Revenue Yield: Treemap visualizing total revenue by room category.
4. Dataset Provenance & Open Mirrors
- 🌐 Primary Origin: Kaggle: Hotel Reservations Classification Dataset (by Ahsan)
- 🤗 Hugging Face Mirror: ahsan81_hotel-reservations-classification-dataset (by jason1966)
- 💾 Local Dataset Source:
D:\courses\Data Analysis 26-27\Hotel Reservations.csv(3.09 MB, 36,275 rows) - 📖 Master Data Dictionary: Hotel Reservations Dataset Documentation
- 📁 Student Practice Workbook:
11_Demos_and_Workbooks/02_Data_Management/Hotel_Reservations_Demo.xlsx