Predictive analytics represents a critical advancement in Local Digital Twins (LDTs), shifting the focus from merely describing past data to anticipating future trends. This capability allows city administrations to proactively design interventions that address both immediate needs and long-term goals, enhancing urban resilience and sustainability in pivotal areas such as mobility, energy, and environmental management. The proposed solution involves implementing predictive analytics within LDTs to enable cities to simulate, optimize, and forecast future scenarios. Techniques like linear regression and decision trees make these predictive models accessible and interpretable, supporting scalable adoption. These solutions bridge the gap between data observation and actionable insights, aligning predictive analytics with city planning and policy-making needs. This learning unit focuses on teaching the implementation of predictive analytics in digital twins using simple forecasting methods, emphasizing real-world applications like Civitas CORE for visualizing urban trends. By engaging learners in hands-on scenario planning and simulation exercises, this unit encourages them to explore predictive analytics as both a technical tool and a means to foster community resilience and public value.