Primary competition visual

Economic Well-Being Prediction Challenge

Helping Africa
2000 Points
Completed (~5 years ago)
Prediction
754 joined
140 active
Starti
16 Apr 21
Closei
15 Aug 21
Reveali
15 Aug 21
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ASSAZZIN
1st Place Solution
Notebooks · 16 Aug 2021, 11:42 · edited 19 minutes later · 5

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 .

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Our Solution Pipeline :

  • Data Processing :

1. Apply frequency Encoder to : 'Year','country','urban_or_rural' Columns .

2. Combine similar Features : 'ghsl_built' ,'landcrover','landcover_water' ( we sum them )

  • Modeling :

1. Using a Cross-validation strategy with K=10, We trained an LGBM Model ,

2. test predictions were clipped between 8 percentiles and maximum .

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Don't Forget to star the Repository, So We'll keep sharing my Solution Code

Discussion 5 answers
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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

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ASSAZZIN

Clipping can give a boost from 0.102901275765604 to 0.102774507242397 .

It's Not a huge difference :)

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well, I also didn't use LGBM. Instead I used Catboost. Nevertheless I find your approach very useful.

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Koleshjr

Thank you ....

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Insightful, thanks for sharing

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