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.

A predictive model to classify maternal health risk levels (High vs Low) during pregnancy using key clinical and physiological parameters. It leverages the tidy models framework in R to build, tune and evaluate multiple classification models with a focus on achieving high predictive performance for early risk identification.