Predicting Flight Prices with Machine Learning: A Multicollinearity-Based Feature Selection Approach
Graduation Year
2023
Date of Submission
4-2023
Document Type
Campus Only Senior Thesis
Degree Name
Bachelor of Arts
Department
Mathematical Sciences
Reader 1
Mark Huber
Abstract
This project utilizes machine learning to forecast flight prices by analyzing flight data from Southern Asian airlines. Several techniques are used during feature engineering to handle multicollinearity before a random forest regressor model is trained on the data. The methodology of this project follows closely to that found in a Skillcate AI project while also offering model comparison and options for model improvement, such as API and cloud database implementation. Limitations of simpler machine learning algorithms are highlighted through the discussion of several metrics used to evaluate model performance and correctness.
Recommended Citation
Monzon, Daniel, "Predicting Flight Prices with Machine Learning: A Multicollinearity-Based Feature Selection Approach" (2023). CMC Senior Theses. 3294.
https://scholarship.claremont.edu/cmc_theses/3294
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.
