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Demand Planning: 142 Elite SKU Analysis
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.
Demand Forecasting using Machine Learning Models
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.
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J. Hu MS Thesis Timeline
Unit 13 R Project
practicing multiple regression with the penguin dataset
