Analysis of hotel booking data to understand seasonal demand patterns, customer behavior, cancellations, and revenue KPIs, with the objective of supporting pricing strategy, capacity planning, and operational decision-making in the hospitality domain.
- Source: Hotel Booking Demand dataset
- Records: ~63,000 bookings
- Hotel Types: City Hotel, Resort Hotel
The analysis examines monthly demand trends, differences between hotel types, cancellation behavior across customer segments, and key hospitality KPIs such as ADR and RevPAR.
Insight:
City hotels consistently receive higher booking volumes, while both City and Resort hotels exhibit strong seasonal peaks during summer months.
Insight:
Transient customers contribute the majority of cancellations, whereas contract and group customers demonstrate higher booking reliability.
An interactive Tableau dashboard was built to track core hospitality KPIs and operational metrics.
Dashboard Highlights
- Total Bookings: 63,353
- Average Daily Rate (ADR): 106.3
- Revenue Per Available Room (RevPAR): 394.4
- Seasonal demand and parking trends
- Customer and geographic segmentation with interactive filters
🔗 Tableau Public Dashboard:
https://public.tableau.com/views/HotelBookingAnalysis_17215875929250/HotelBookingAnalysis
- Python (Pandas, NumPy, Matplotlib)
- Jupyter Notebook for exploratory analysis
- Tableau for interactive dashboards
- Identified seasonal demand patterns to support capacity and pricing decisions
- Highlighted high-cancellation customer segments for policy optimization
- Enabled dashboard-driven monitoring of revenue and occupancy KPIs