train delay prediction using machine learning

This project focuses on predicting train delays using machine learning techniques to enhance operational efficiency and improve the overall passenger experience. The core of the system is a trained XGBoost regressor model, which predicts train delay times based on various input features, including the distance between stations, weather conditions, day of the week, time of day, train type, and route congestion. The model is trained on historical train delay data and is deployed in a web-based interface built with Flask, allowing users to input relevant information and receive real-time predictions. The web application also includes user authentication, enabling users to register, log in, and interact with the prediction system. The predicted delay is displayed in either minutes or hours and minutes, based on the value of the prediction. This solution can be used by transportation authorities to forecast delays and optimize train scheduling, improving service reliability and customer satisfaction. The project demonstrates the power of machine learning for real-world applications in the transportation sector, offering a practical and accessible tool for train delay management.