Congratulations to the winners. I'd love to hear how those with top scores handled their cross-validation and avoided overfitting as well as feature engineering techniques used.

Congratulations to all. Thanks to my teammate @Koleshjr. It was really an amazing competition we learned a lot from it and thanks to @zindi for hosting such a competition.

Our approach is

• Deal with missing values
• Drop unnecessary columns
• Drop outliers (rows with the same data but different categories)
• Count encoding & new features
• MERCHANT CLUSTERING to 10 categories
• Aggregation and feature combination
• Automatic clustering using KMeans from CountVectrozer
• Decomposition of features
• Oversampling using SMOTE
• StratifiedKFold with 5 split
• Voting classification and model stacking