Denoising autoencoders will be your bet in this case. I don't have a book handy for this case. They are good at reconstruction and calculate a good latent representation. Just replace your missing feature with the mean or a fixed value.
Since you mentioned that there is a linear relationship between Age and Bandwidth, you could train a simple linear model i.e linear regression on your dataset to estimate the bandwidth based on Age. Once you have figured out the relationship you can use it to impute the missing bandwidths.
Just by eyeballing the data i would think the number of visitors for store A on jan 4th is around 110. Basically it is always approximately half of Store B on any given day.
It seems like the number of visitors across stores are correlated, so you could potentially use simple linear regression to get a reasonable estimate for any given day.
I would like to add that apart from ML libs being robust to (can handle rather) Nan values, such as XGBoost, kNN implementations, there are also missing data imputation techniques. Which once you implement them you can try any ML algorithm. However, there are a few of them and you have to see what works best. For reference, you can look at hot deck ...