Train a RandomForestClassifier on user-product interaction features to predict future interaction.
Split the data (e.g., 80% training, 20% testing). 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: To help you develop this further, could you
What is in the (e.g., user-save data, product metadata)? Assuming the goal is to develop a feature
Create vectors based on description, category, and seller [1, 3].
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 .
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