Departure Delay Prediction using Machine Learning

Abstract

Abstract: The project aims to investigate forecasting strategies for forecasting weather-related planes delays. In order to plan ahead and reduce the effects of disruptions, it is essential for airlines and travelers to be able predict such delays with accuracy. The research focuses on developing an ensemble model-based machine learning flight delay prediction system. The Airline dataset is subjected to the use of three distinct machine learning methods: Random Forest Clasifier, k-nearest-neighbor (KNN), and Support Vector Machine (SVM). The suggested approach is then assessed for efficiency and outcomes using a comparative analysis