This project demonstrates a 'Global Model' approach to time-series forecasting using Python and Quarto. It features a robust pipeline for data preprocessing, multi-model benchmarking (Linear Regression, KNN, AdaBoost, SVR), and out-of-sample performance evaluation. The final implementation utilises Support Vector Regression (SVR) to handle non-linear demand spikes, achieving a stabilised forecast across a diverse dataset of over 3,400 observations.

J. Hu MS Thesis Timeline

practicing multiple regression with the penguin dataset