Predicting Flight Prices with Machine Learning: A Multicollinearity-Based Feature Selection Approach
Date of Submission
Campus Only Senior Thesis
Bachelor of Arts
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.
Monzon, Daniel, "Predicting Flight Prices with Machine Learning: A Multicollinearity-Based Feature Selection Approach" (2023). CMC Senior Theses. 3294.
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.