Predicting and Evaluating Train Occupancy in Belgium using Weather Data by developing Ensemble Stacking Classifier

This research aims to predict train occupancy of Belgian trains by utilizing the Belgium trains dataset and Antwerp city weather dataset. The goal is to optimize train schedules and improve the overall efficiency of the Belgian train transportation system. Leveraging historical data and weather conditions, a predictive model, particularly the Stacking Classifier 1, was employed to accurately forecast train occupancy. The integration of weather data in the model proved crucial for making informed decisions in resource planning and train schedule optimization. By enhancing predictions and efficiency, this project has the potential to significantly enhance passenger experience, reduce costs, and minimize environmental impact in the Belgian railway system.