This project delivers a comprehensive Customer Value Segmentation analysis for our online education platform, utilizing transaction data spanning 9,935 orders across a 12-month period (January 1, 2019 – December 31, 2019). By implementing a Recency, Frequency, and Monetary (RFM) framework, the analysis moves past aggregated metrics to map true student and institutional behavior. The objective is to transition from uniform marketing to high-precision, automated B2C workflows and high-touch B2B account management.

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.

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.