Authors

Description

When implementing machine learning models, the scarcity of high-quality data poses a common challenge, particularly in industrial settings, often leading to project cancellations. Enhancing the quality or quantity of available data is typically costly or challenging to achieve. One strategy to achieve this is agent-based modeling, offering an alternative and promising approach to address this issue. This methodology enables the simulation of complex systems and the generation of artificial datasets safely, and affordably. Imagine the ability to generate realistic data to fuel your machine learning models, without the challenges associated with collecting real-world data. Agent-based modeling offers this potential, emerging as a valuable tool to overcome hurdles in the development of machine learning projects.