Multiplex Regulation System With Personalised Recommendation Using ML

The main aim of this research work is to conduct a feasibility study and requirement analysis for a Multiplex Management system (MMS). This research work includes the design diagrams that facilitate the production of the software and its use in making a frontend and backend for the system according to the elicited requirements, by integrating them. Its alpha version, which strictly adhering to relevant software engineering practices that test the software with suitable methods has also developed. It automates various functions of Multiplex Theater with particular emphasis on ticket reservation, show screening, personnel management, statistical report generation, and increasing ticket sales by giving personalized movie suggestions by learning from the previous booking history or characteristics of users. MMS is a Web application that provides different interfaces for different stakeholders like users, admins, and employees. The admins can add movies and schedule screenings and evaluate the booking statistics and the users and employees can book and cancel tickets. The system also analyzes various factors using Machine Learning to suggest movies to the User to maximize their chances of booking them. The data and model generated from this system are also a precious commodity that can be leveraged successfully to bring in large gains for the Service.