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Abstract
Revenue management in the hospitality industry is essential for maximizing profitability and ensuring competitive positioning in an increasingly dynamic market. This project introduces an interactive Hotel Revenue Management Dashboard, developed using Python and Streamlit, designed to provide real-time data analysis and visualization for hotel operators. The dashboard integrates various financial and operational metrics, including total revenue, booking trends, guest satisfaction ratings, and competitor pricing insights, allowing hotel managers to make well-informed strategic decisions. The system leverages a structured data processing pipeline, utilizing Pandas for data wrangling, Plotly and Seaborn for visual analytics, and Streamlit for an intuitive user interface. The dashboard incorporates interactive filters, enabling users to analyze revenue trends across different hotels, track seasonal fluctuations, and compare key performance indicators (KPIs) over specified time frames. A range of visualizations, such as line charts, bar charts, histograms, and scatter plots, enhance data interpretability, providing a clear picture of the hotel's financial health and market position. This study explores the methodology, design, and implementation of the revenue management dashboard and evaluates its effectiveness in providing actionable business insights. The findings highlight the potential of data-driven decision-making in hotel revenue management, emphasizing the importance of real-time monitoring and strategic forecasting. Future enhancements may include the integration of machine learning models for predictive revenue analysis, real-time API connections for continuous data updates, and advanced business intelligence tools for deeper market analysis. Ultimately, this project demonstrates the transformative impact of data visualization and analytics in optimizing hotel revenue strategies and enhancing operational efficiency.