Wanelo_rf.7z | BEST ✧ |

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

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

Train a RandomForestClassifier on user-product interaction features to predict future interaction. Wanelo_RF.7z

This feature will analyze the Wanelo_RF.7z data to suggest products tailored to specific user preferences. 1. Data Preparation & Engineering

Create vectors for users based on categories saved, price points, and interaction frequency. Handle missing values, remove duplicate entries, and format

The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation

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

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