Goal: Tunning hyperparamters for higher accuracy of Product Arrival
Summary:
We chose a Kaggle dataset for analyzing what are the top 5 important features among 20+ from an E-commerce dateset in Kaggle. After dealing with dataset impbalance, we built different classical models and tune hyperparamters to improve the accuracy of the binary results, whether the product is arrival after feature engineering.
Glad to discuss more by Email lz2871@columbia.edu