I'm trying to implement a product categorization ML model on a dataset with the following structure: Data sample
I want to my model to be able to predict the correct category that the product should fall under, based on product description and name.
However, I will be implementing this together with a GUI which allows some user input.
For example, a new product name with a description gets added to the table: New entry before feedback training
The user will be presented with the following options (completely made these up) and has to select one:
Kitchen furniture - 65%
Home decorations - 29%
Kitchen Appliances - 6%
User will click on 'Home decorations'. This gets fed back to the model. Next time the model encounters something similar, such as: New entry after feedback training
The user will be presented with more accurate predictions, where this time they have the same options to choose from, but with different predicted accuracy:
Home decorations - 70%
Kitchen furniture - 20%
Kitchen appliances - 10%
Therefore, the model has learned from that feedback and has become more accurate. I've done some research around this and it has pointed towards Reinforcement Learning. However, I couldn't find anything too similar and I am not THAT skilled in ML, so please point me in the right direction in terms of what Python libraries to use, what ML models to look at and maybe even previous implementations.