Development of a Smart-Based Online Ticket Booking Application Using Machine Learning Algorithms
Keywords:
Smart-based ticket booking, Machine learning algorithms, Personalization, Dynamic pricing;, User experienceAbstract
The development of a smart-based online ticket booking application using machine learning algorithms aims to address inefficiencies, lack of personalization, and user experience issues prevalent in traditional ticketing systems. This research integrates advanced machine learning techniques to enhance system functionality by offering personalized ticket recommendations, dynamic pricing strategies, real-time updates, and robust security measures. The application leverages user behavior data and predictive models to improve decision-making for both users and service providers. The study employs a comprehensive development methodology, including algorithm design, system implementation, and performance evaluation. Key performance indicators such as algorithm accuracy, user satisfaction, scalability, and security are measured through statistical analysis and user feedback. Results demonstrate significant improvements in recommendation accuracy, demand forecasting, and system responsiveness during peak usage periods. Additionally, enhanced fraud detection mechanisms and accessibility features ensure inclusivity and data security, fostering higher user trust. This research contributes to the field of online ticket booking by introducing a scalable, intelligent platform that redefines the user experience while optimizing operational efficiency. It highlights the potential of machine learning in transforming digital services, setting a foundation for further innovation in the ticketing and e-commerce sectors.
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