Train Waitlisted Ticket Confirmation Prediction Using Machine Learning

The issue of predicting the confirmation of waitlisted tickets in the railway sector presents a significant challenge for both passengers and authorities. This research utilizes historical booking records, cancellations, passenger preferences, and travel patterns to develop precise predictive models to address this issue. A Machine Learning framework, specifically the Light Gradient Boosting Machine (Light GBM), is proposed in this paper to accurately forecast the likelihood of waitlisted train tickets being confirmed. This algorithm has displayed an astonishing accuracy rate of 96.67%. This research aims to address the complexities of this problem while also investigating the potential enhancements in data availability through online ticketing systems and digitization.