Handle missing values, remove duplicate entries, and format timestamps [1, 2]. Feature Engineering:
Create vectors based on description, category, and seller [1, 3].
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:
Based on the filename "Wanelo_RF.7z," this appears to be an archive containing data related to (a former social shopping platform) likely for a Random Forest (RF) machine learning model .
Unzip Wanelo_RF.7z to access the underlying CSV or data files (e.g., user behaviors, product details, save history).
The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation
Handle missing values, remove duplicate entries, and format timestamps [1, 2]. Feature Engineering:
Create vectors based on description, category, and seller [1, 3]. Wanelo_RF.7z
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
Based on the filename "Wanelo_RF.7z," this appears to be an archive containing data related to (a former social shopping platform) likely for a Random Forest (RF) machine learning model . To help you develop this further, could you
Unzip Wanelo_RF.7z to access the underlying CSV or data files (e.g., user behaviors, product details, save history).
The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation