The problem AI Based Shopping Assistant solves

-> This Model recommends the consumer on the basics of it's history and search habits which will ease the selection process and estabish a user friendly application.
-> Project is user - centric that provides the individual to enjoy AI experience.
-> As the dataset is clustered in most suitable keywords giving the idea of user purchasing habits , and later the model suggests the products to the consumer in the most suitable way, hence providing a better shopping experience.

Challenges we ran into

-> We faced the basic challange of getting the suitable dataset for an individual , hence we started from scrach and building the suitable dataset for the model.
-> We started from the RNN model , but came to conclusion that it predicts the nextr move of the consumer , but we didn't want that because we wanted to recommend the user according to it's preference and user's history of searches and purchase.
-> The clustering technique we used was difficult to process . we faced challanges to modified accordingly.
-> Hierarchical Clustering is used in the model , as the dataset is clustered in most suitable keywords giving the idea of user purchasing habits , and later the model suggests the products to the consumer in the most suitable way, hence providing a better shopping experience.