Optimizing Data-driven Project Duration Prediction through Machine Learning Approaches
Accurately predicting project duration is vital for effective management in large-scale engineering and product development. This thesis analyzes a dataset from Ericsson, covering over 8,000 projects from 2016 to 2025, to develop machine learning models for predicting project duration across various telecommunications programs. It also identifies key features that influence project timelines. Aft
