I am developing a predictive model for a water supply network that involves 20 influencing points. However, I only have historical data for 10 out of these 20 points.
I would like to know how to approach building a predictive model given this incomplete dataset. Specifically:
What methods can I use to handle the missing data for the remaining 10 points? Are there any standard techniques or best practices for dealing with missing data in such scenarios?
How can I effectively incorporate the data from the 10 points I do have into the model? What strategies can I employ to ensure that the available data is utilized efficiently to make accurate predictions?
Are there specific techniques or models that can help in making predictions despite having incomplete data? I am interested in methods that can manage and leverage incomplete data effectively.

“I don’t have a specific method yet.”