All thanks to my Amazing teammate @CalebEmelike, we were able to secure 1st place in this interesting competition.
Here is a repository that contains Solution Code on how to reach 1st Place in this Challenge.
Repository Link : https://github.com/ASSAZZIN-01/Economic-Well-Being-Prediction .
Note: this is not our Final solution! , but it's another solution code on how to reach 1st place .
Our Solution Pipeline :
1. Apply frequency Encoder to : 'Year','country','urban_or_rural' Columns .
2. Combine similar Features : 'ghsl_built' ,'landcrover','landcover_water' ( we sum them )
1. Using a Cross-validation strategy with K=10, We trained an LGBM Model ,
2. test predictions were clipped between 8 percentiles and maximum .
Don't Forget to star the Repository, So We'll keep sharing my Solution Code
seems like clipping made a huge difference in perfomance in the private lb. I basically did what you have done and had a similar local validation score
Clipping can give a boost from 0.102901275765604 to 0.102774507242397 .
It's Not a huge difference :)
well, I also didn't use LGBM. Instead I used Catboost. Nevertheless I find your approach very useful.
Thank you ....
Insightful, thanks for sharing