https://github.com/hemangsharma/hotel-revenue-booking-analysis
This project provides a comprehensive revenue and reservation analysis for Highfield Hotel using historical data exported from booking systems and internal revenue reports. The goal is to derive actionable insights to improve room profitability, understand booking patterns, and support data-driven decision-making.
https://github.com/hemangsharma/hotel-revenue-booking-analysis
analysis data-analysis data-visualization hotel
Last synced: 3 months ago
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This project provides a comprehensive revenue and reservation analysis for Highfield Hotel using historical data exported from booking systems and internal revenue reports. The goal is to derive actionable insights to improve room profitability, understand booking patterns, and support data-driven decision-making.
- Host: GitHub
- URL: https://github.com/hemangsharma/hotel-revenue-booking-analysis
- Owner: hemangsharma
- Created: 2025-07-09T08:59:45.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-07-10T00:50:05.000Z (4 months ago)
- Last Synced: 2025-07-10T07:45:35.905Z (4 months ago)
- Topics: analysis, data-analysis, data-visualization, hotel
- Language: Jupyter Notebook
- Homepage:
- Size: 551 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hotel Revenue & Booking Analysis
## Project Overview
This project provides a comprehensive revenue and reservation analysis for **Highfield Hotel** using historical data exported from booking systems and internal revenue reports. The goal is to derive actionable insights to improve room profitability, understand booking patterns, and support data-driven decision-making.
---
## Objectives
- Analyze room-wise profitability and occupancy.
- Identify high-performing and underperforming rooms.
- Understand booking patterns, cancellation trends, and guest behavior.
- Detect opportunities to optimize pricing or improve occupancy.
- Automate the monthly reporting process across Excel, Power BI, and Tableau.
---
## Data Sources
- `room_income.csv`: Internal revenue and room utilization data.
- `bcom_data.csv`: External Booking.com reservation data (arrival, payment, commission).
Both are located in the `data/` folder.
---
## Project Structure
hotel_data_analysis/
|
|- data/ # Raw data (CSV)
|── notebooks/ # Jupyter notebooks for data cleaning and analysis
|── outputs/
| |── reports/ # README, summary reports
| |── graphs/ # Visualizations
| |── excel/ # Excel dashboards
|── dashboards/
| |── power_bi/ # Power BI reports (.pbix)
| |── tableau/ # Tableau workbook (.twbx)
|── scripts/ # Automation scripts
---
## Tools Used
- **Python (Pandas, Matplotlib, Seaborn, Plotly)** — For data wrangling and analysis
- **Excel** — Quick tabular dashboard and trend views
- **Power BI** — Interactive reporting
- **Tableau Public** — Visual storytelling
- **Jupyter Notebooks** — All cleaning and processing steps
---
## Key Insights Delivered
- Net profit per room and room type
- Commission losses and cancellation rate from Booking.com
- Revenue leakage patterns
- Room occupancy vs profitability heatmaps
- Recommendations for tariff optimization
---
## Contact
For improvements, issues or collaboration, please contact the data analytics team.