Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Taryn Trimble, Senior Ioannis Paraschos, Junior Eshna Bhargava, Junior
Lead Presenter's Name
Taryn Trimble
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Mihhail Berezovski
Abstract
Partnering with OneSky Flight LLC, this project aims to develop a flight time predictor using various machine learning methods. OneSky Flight LLC's goal is to provide IT support for various private jet companies. Their flight time predictor is mainly used for customer satisfaction and aircraft turn-over efficiency. This project involves creating a potential improvement on the current flight time predictor they use now. Six months of flight data was provided by OneSky Flight LLC; it included attributes such as origin, destination, aircraft type, departure time, and arrival time. The two primary methods tested were neural networks and decision trees. Each method was tested with varying architectures and data structures to determine accuracy. The resulting analyses of the architectures found the XGBoost decision tree to be the highest performing model. Using the results of the architectures, an ensemble model can be developed that incorporates the use of both neural networks and decision trees to further increase the accuracy of the predictor.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Predicting Flight Time Using Machine Learning Methods
Partnering with OneSky Flight LLC, this project aims to develop a flight time predictor using various machine learning methods. OneSky Flight LLC's goal is to provide IT support for various private jet companies. Their flight time predictor is mainly used for customer satisfaction and aircraft turn-over efficiency. This project involves creating a potential improvement on the current flight time predictor they use now. Six months of flight data was provided by OneSky Flight LLC; it included attributes such as origin, destination, aircraft type, departure time, and arrival time. The two primary methods tested were neural networks and decision trees. Each method was tested with varying architectures and data structures to determine accuracy. The resulting analyses of the architectures found the XGBoost decision tree to be the highest performing model. Using the results of the architectures, an ensemble model can be developed that incorporates the use of both neural networks and decision trees to further increase the accuracy of the predictor.