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Project # 28
Predicting Airline Arrival Times and Delays using Machine Learning
| Team Lead: | Zachary Bielss |
| Other Team Members: | Omar Ulysses Soldevilla | Tracy Nguyen | Felton Walton | Syed Ali | Jasmine Pankratz | Gabriel Mireles |
| Faculty Advisor(s): | Dr. Aera Kim LeBoulluec |
| Department: | IMSE |
| Project Type: | Other Undergraduate |
| Title: | Predicting Airline Arrival Times and Delays using Machine Learning |
| Abstract: | The consistent and timely arrival of flights is a significant concern for both airlines and passengers as delays can result in significant financial losses and inconvenience. This project aims to develop a machine learning-based model to predict airline arrival times and delays. By leveraging historical flight data, the model will identify patterns that impact flight arrival times. Multiple machine learning algorithms will be evaluated to determine the most accurate predictive model. This tool aims to enhance operational efficiency for airlines and improve the travel experience for passengers by providing reliable delay forecasts. It also seeks to demonstrate the potential of machine learning in addressing real-world transportation challenges and improving service reliability. |
| Poster: | View Poster #28 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |