[Project Status: Completed] | [Grade: 95%]
This project is an in-depth analysis of Transport for London (TfL) passenger footfall data, submitted as the final coursework for the MTH6139 Time Series module at Queen Mary University of London. The primary goal was to apply statistical forecasting techniques to model historical trends and predict future passenger numbers.
Successfully identified and modelled strong weekly and seasonal patterns in passenger data, corresponding with commuter behaviour and public holidays.
Implemented a Prophet forecasting model in R, which accurately predicted short-term passenger footfall with a high degree of confidence.
The final analysis, including methodology and visualisations, was awarded a grade of 95%.
Language: R Key Packages: prophet, tidyverse (for data manipulation), ggplot2 (for visualisation)
The complete report, including all code and visualisations, has been published on RPubs.
