Forecasting monthly passenger volumes for Changi Airport

This is the final project for my "Forecasting and Risk Analytics" at the University of Cincinnati. For this project, my teammates and I worked on multiple datasets that contain data of passengers and Singapore's GDP before, during and after the pandemic in 2020.

The objective is to analyze and predict the monthly passenger volume at Changi Airport in correlation with Singapore's GDP using simple linear regression and ARIMA model using R and Python.

We were able to generate 3 models and found that ARIMA model is the best model with the lowest risks in predicting passenger volumes. Our simple linear regression also fits our passenger data very well. Airport executives may use this to plan expansions and additional terminals in the far future, as they can expect passenger volumes to increase as GDP and the Singapore economy grow.