Imagine you are working on a project where the dataset provided is incomplete and contains numerous missing values. How would you approach this situation to ensure the analysis remains robust and reliable?

Suppose you are tasked with building a predictive model, but after deployment, the model's performance starts to degrade. What steps would you take to diagnose and address this issue?

You are collaborating with a team of non-technical stakeholders who are skeptical about the insights derived from your data analysis. How would you communicate your findings to ensure they understand and trust the results?