Primary competition visual

African Credit Scoring Challenge

Helping Africa
$5 000 USD
Completed (~1 year ago)
1959 joined
1022 active
Starti
Nov 29, 24
Closei
Jan 12, 25
Reveali
Jan 13, 25
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josh_amayo
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0.66 Private approach
Notebooks · 16 Jan 2025, 11:46 · 10

Here's my notebook for a mid-level private score of 0.66. https://www.kaggle.com/code/joshuaamayo/zindi-credit-scoring .Posting for any beginners to follow through my comments and reflections in the markdown, and for the seasoned competitors to give their tips/questions in the comments, as this was my first ever prized competition. Thanks!

Discussion 10 answers

It seems that the link is not working. Could you check, please?

16 Jan 2025, 14:22
Upvotes 0

maybe it's the final character (".")

16 Jan 2025, 14:24
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josh_amayo
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Thanks for letting me know! The link is working now.

Interesting. We had a similar approach

I split my notebooks into 5 notebooks: https://github.com/MakalaMabotja/credit-default-prediction/tree/main/experiments

It seems that if you didn't use the Ghana target = 1 trick that's been circulating then you probably built an overfit model with a LB F1 score of around 0.7

16 Jan 2025, 14:36
Upvotes 1

in the github, the following notebooks seem not to be working

5-advance_model_building.ipynb

6-final_model.ipynb

could you check, please?

Thanks!

No they're just empty.

I was experimenting with ensemble methods (Voting and Stacking) on top of those already used in models available from XGB & RandomForest (boosting and bagging respectively).

All of this is available in the 3rd notebook. I just haven't cleaned it up yet but I thought I would share my thought process in case it made to some one else other than me.

I still needed to do some more feature engineering (notebook 4) but I ran out of time before getting back to work. After which I wanted to see if I can combine a basic logistic regression model (best at accurate minority class predictions - recall) and tree models (best at majority class predictions - precision) to see if I can't get a more generalized model

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josh_amayo
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Yes, I didn't use a stratified split so it's possible that some customer ids may have leaked to my val set