This repository houses a Tableau dashboard project focused on hospitality data. The dataset, obtained from a codebasics data analytics portfolio challenge, includes five clean CSV files representing ATLIQ hotel properties across five Indian cities.
After joining and processing data, the dashboard incorporates over 20 columns, addressing questions spanning booking trends, customer behavior, platform performance, revenue generation, occupancy, room performance, cancellations, and geographical analysis.
Three essential calculated fields enhance visualization insights:
Net Revenue: Considering revenue realized and generated, accounting for possible cancellations.
Occupancy Rate: Evaluating hotel room efficiency using guest and capacity data.
Booking Time: Analyzing days between booking and check-in.
A hierarchical structure allows drilldown functionality, categorizing data by room category, class, and specific ATLIQ properties.
Booking Trends: Peak on Sunday and Friday, consistently over 1.2 million daily bookings.
Revenue Analysis: Varied net revenue distribution across cities, revealing outliers and median values.
Occupancy Insights: Presidential rooms lead, Elite rooms have the least occupancy.
Geographical Patterns: Mumbai records the highest booking frequency.
Customer Behavior: Higher wait times correlate with higher ratings, potentially indicating loyalty.
Cancellation Analysis: A successful split with around 33.4 thousand cancellations.
Room Class Performance: ATLIQ Seasons' Presidential room class contributes the most to net revenue.
Platform Comparison: Varied average ratings across booking platforms.
This insightful project applied classroom learnings to a relevant business case in hospitality management. Utilizing a blue-grey color scheme, the dashboard incorporates joins, calculated fields, diverse visuals, labels, interactive filters, and hierarchical functionality.
Feel free to explore the detailed documentation in the included Word file, along with the data folder and the Tableau dashboard. Contributions and feedback are always welcome! 🏨📊