I have a dataset of streets - and each street contains several houses.
street_id | house_id | number of calls | number of people living | etc... 1 | 1 | 10 | 5 1 | 2 | 3 | 1 2 | 3 | 4 | 2 2 | 4 | 15 | 19
- My goal is to find for each street, the most popular house.
- I have GT - streets with several houses, and for each street, which house was the most popular house.
- For the data example above - the results should be (street 1 -> house 1, street 2 -> house 4) because houses #1, #4 are the most popular in their street.
- Popular will be determined with classification algorithm - we have much more features than the 2 above (they are just for example). That's why we have the GT for - so our algorithm can learn what popular is.
We can find the most popular street using features about each house -
- number of calls made from house
- number of people living in house
But we don't need to find all of the popular houses from all of the houses - we need for each street, to find the most popular house.
Do you know how can I solve this task?
I've thought about regular classification, but that would results giving all of the popular houses, and not one for each street.
Thanks in advance!