As you know the data type is one of the most important factors in selecting the Machine Learning algorithm. For example, K-means should not be employed for categorical data. I have a csv file containing road network segment features. One of my feature is speed limit which shows eligible speed in a road link. For example, we see the marks and signs in road which determine the eligible speed for driving. I would be grateful if you let me know the speed limit data is continues or categorical data (nominal data)?
Speed limit could be treated as either continuous or categorical. It depends on the purpose of the model, and your expectations of how it will be applied.
Let's say all the road segments in your data have one of two speed limits: 50 kmph, or 80 kmph. Now, you might want to predict the outcome for some road segments that have speed limits that are different from those values, e.g. 60 kmph. If so, it would be good to treat the speed limit as continuous, so that the model can interpolate between the values in the training data.
On the other hand, if your primary interest is in making inferences about the difference between 50 kmph and 80 kmph segments, then you could code the speed limit as nominal.