Abstract

This study focuses on developing a predictive framework for estimating cloud migration time from Netezza to the Azure Cloud environment. As organizations increasingly adopt cloud-based infrastructure to enhance scalability and performance, accurate migration time estimation becomes a critical planning factor. The study leverages machine learning regression techniques—Gradient Boosting Regression (GBR) and Hist Gradient Boosting Regression (HGBR)—to model migration complexity and duration based on key system attributes.