Airport Analyzer: A Machine Learning Approach to Predict Flight Performance
Flight delay is a critical operational problem around the world. Flight delay is concerned with numerous features such as weather, excessive traffic, runway construction and other factors. The proposed machine learning model is developed to provide prediction of flight departures and arrival delays based on weather information of about 10 days by collecting real time datasets. The model Airport Analyzer achieves high prediction accuracy of 80% by using Support Vector Machine classifier. Experimental results showed that the proposed model performed well compared to other classifiers.