Predicting Operator’s Choice During Airline Disruption Using Machine Learning Methods

Sammanfattning: This master thesis is a collaboration with Jeppesen, a Boeing company to attempt applying machine learning techniques to predict “When does Operator manually solve the disruption? If he chooses to use Optimiser, then which option would he choose? And why?”. Through the course of this project, various techniques are employed to study, analyze and understand the historical labeled data of airline consisting of alerts during disruptions and tries to classify each data point into one of the categories: manual or optimizer option. This is done using various supervised machine learning classification methods.