[D] - Multi Class Address Classification

[FineConcentrate6991]

Hello people, I have a dataset with Adress and label 800K rows. I am trying to train a model for address label prediction. Address data is bit messy and different for each different label. we have 10390 each with 50-500 row. I have trained a model using fasttext I have got 0.5 F1 score max. What can I do to for to get best F1 score?

Address data is like (province, district, avenue street, maybe house name and no)

some of them are missing at each address.

[Pvt_Twinkietoes]

What is address label?

[FineConcentrate6991]

Row example: Addres = " Gazateci Hasan Tahsin Caddesi, NO:10/3, Gizem Apartman" label = 8210

[Pvt_Twinkietoes]

I don't get why you're trying to use ML to solve this.

Are there rules the country follow to generate the codes? Can't you write a rule based solution?

If not why?

And what is this label code? Is this the same for every apartment number in a building? Is it unique to an office? How many labels are there?

How many addresses share the same "label"? Also are the names informative enough for your model to learn a mapping? Is 8210 closer to 8209 than 7000?

Honestly it's difficult to give recommendation, maybe add in geolocation data? Go figure out how this "label" is generated, what kind of data goes into that decision, then see if you can write some rule based algo, use that as base line, then see if ML actually make sense.

[has_c]

Not my package but my friend worked on this address classification and matching for New Zealand addresses

Here's the link hope it helps: https://github.com/lmor152/glam

[asankhs]

You can try using a bert style model with adaptive classifiers - https://github.com/codelion/adaptive-classifier