Standard Bank Tech Impact Challenge: Xente credit scoring challenge
2000 Zindi Points
Can you predict the likelihood of credit default of ecommerce clients?
30 August–1 December 2019 23:59
267 data scientists enrolled, 66 on the leaderboard
Data exploration, trying to understand the problem and feature ideas Notebook.
published 5 Nov 2019, 17:15
edited 14 days later

I've just started working on this data, here is my repo for this challenge where i'm trying to understand the data and the problem to solve. I will be updating this notebook as soon as i make some progress.

https://github.com/blenzus/StandardBankLoanDefault/blob/master/Standard_Bank_EDA.ipynb

https://github.com/blenzus/StandardBankLoanDefault/blob/master/Standar_Bank_BaselineModel.ipynb

edited 1 minute later

Update : Exploration is somewhat well developed. Posted a starter notebook in Python ( Jupyter Notebook ) that gets you around the 30th spot. Intuition behind the features generated in the baseline model is in the other notebook in order to understand the logic behind such features.