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

Given the "RF" in the filename, a Random Forest classifier is appropriate for predicting the likelihood of a user saving a product [2, 3].

Unzip Wanelo_RF.7z to access the underlying CSV or data files (e.g., user behaviors, product details, save history).

Create vectors based on description, category, and seller [1, 3].

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

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:

Handle missing values, remove duplicate entries, and format timestamps [1, 2]. Feature Engineering: