Wanelo_rf.7z

Create vectors for users based on categories saved, price points, and interaction frequency.

Use Precision@K and Recall@K to evaluate how many of the top-K recommended products were actually relevant to the user [2, 3]. To help you develop this further, could you tell me: Wanelo_RF.7z

Assuming the goal is to develop a feature (a predictive model or data analysis tool) from this dataset, here is a structured approach to building a [1, 2, 3]. Project: Personalized Recommendation Engine Create vectors for users based on categories saved,

What is in the (e.g., user-save data, product metadata)? or analyze user trends?)

Develop an API endpoint (e.g., /api/recommendations/ ) that fetches the trained model.

What is the ? (e.g., recommend products, predict sales, or analyze user trends?)