The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation
Create vectors based on description, category, and seller [1, 3].
Develop an API endpoint (e.g., /api/recommendations/ ) that fetches the trained model.
What is in the (e.g., user-save data, product metadata)?
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 for users based on categories saved, price points, and interaction frequency.
The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation
Create vectors based on description, category, and seller [1, 3].
Develop an API endpoint (e.g., /api/recommendations/ ) that fetches the trained model.
What is in the (e.g., user-save data, product metadata)?
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 for users based on categories saved, price points, and interaction frequency.
