Application of Machine Learning Algorithms to Predict Flight Arrival Delays

Growth in aviation industry has resulted in air-traffic congestion causing flight delays. Flight delays not only have economic impact but also harmful environmental effects. Air-traffic management is becoming increasingly challenging. In this project we apply machine learning algorithms like decision tree, logistic regression and neural networks classifiers to predict if a given flight’s arrival will be delayed or not. We show that with only three features we were able achieve a test accuracy of approximately 91% for all three classifiers.