Edge Deployment for Real-Time Churn Prediction

Created using ChatSlide

This research project investigates the feasibility of an on-device, offline-capable solution to address latency and connectivity challenges in retail churn prediction. It identifies significant gaps in current on-device inference capabilities and introduces a 7-step pipeline to enhance processing efficiencies. Utilizing the UCI Online Retail II dataset, the study applies RFM and temporal data splits for feature engineering. A model comparison underscores test performance, highlighting...