How to handle addresses of the restaurants to feed the data-set in the ML model? I have data from different restaurants which have also address of the restaurants now I want to predict the food delivery timing based on the given data, now the restaurant address is one of the crucial data which I need to predict the food delivery timing, now my problem is the address is in string format so how do I handle this address to feed the data in the ML model. I have a total of 35 unique addresses if I do one-hot encoding my dataset will be very large and it will take too much time to train, is there anyway except one-hot encoding to handle the addresses of restaurants.

Data Sample-
You can convert each address to a pair of geographical coordinates (latitude, longitude). In this way, you'll have a rough measure of the distance between different location. This can be done with Google Maps, for example.

I suggest you to use compute Manhattan distance between locations, in order to properly estimate travel times within a city.