The capstone purpose is to create a comprehensive case study demonstrating the practical application of data science in the context of railway transportation. By utilizing data science concepts, the project identifies patterns, trends, and correlations within inventory, market conditions, PNR, and scheduling data, leading to informed decision-making for mintage the passenger demand and inventory issues. The case study serves as an educational resource and showcases the potential business impact of implementing a dynamic revenue optimization system. 

Objectives of the Case Study 

1. Develop a predictive model for demand forecasting based on booking class and segment.
2. Implement regression analysis to segment markets and tailor strategies accordingly.
3. Optimize inventory management to minimize losses and meet demand efficiently. 
4. Enable segment-based demand metrics for decision makers to adjust capacity.
5. Analyze PNR data to identify patterns and provide booking forecast.
6. Provide actionable recommendations for maximizing meet travel demand.