Social Event Tracking System with Real-Time Data Using Machine Learning
Muhammad Usman Nazir, Sukhpal Singh Gill · 2024
Social events and gatherings have always been vital to the communal structure. With an ever-increasing amount of social events occurring each year, there is an increasing trend for tech platforms to plan, analyse, and promote events to reach audiences. These platforms, however, are restricted to covering only one aspect of the events, which is the management of online ticketing, whereas other essential data are often neglected. This work develops a social event tracking system with real-time data (SETS). It aims to provide a robust platform on which both the attendees and the organisers of social events in an area can come together to gain useful insights and recommendations based on their personality and demands vis-à-vis providing real-time event analytics across multiple measures. This work utilises a Heroku-based cloud back end and an efficient iOS client application to access the platform and its features.