So, Age ~ Rings and must be predicted from the set of different measures as Diameter, Weight, Height, Length, etc. It is supervised learning task, because of the dataset with relation Result~Features is provided. Simple check shows numbers of rings from 1 to 29 and it is huge range for classification. Another supervised learning algorithm is a linear regression.
EDA (exploratory data analysis) is a first step before building any model and there is the code for loading dataset into memory and plotting several relations, for example Rings~Diameter
This image (as well as other relations like Rings~WholeWeight, etc) shows pretty well difference relations for each sex and the first thought is to apply different regression for each 'sex' or use 'sex' as a factor.
For example, go on with different regression models, we need to construct formula by investigating each relations. For example, there is Rings~WholeWeight relation
Obvious, that for Male and Infant relations has logarithmic trend and it will be logically to add 'log' in formula.
Rings= 8.5398 - 7.6755*Length + 8.7707*Diameter^2 + 1.4837*log(WholeWeight) + 2.0745*log((ShellWeight) -2.3415*log(ShuckedWeight) + 27.8275*Height + 5.9972*VisceraWeight
As was mentioned in task description Age=Rings+1.5