Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data–including telco and transactional information–to predict their clients’ repayment abilities.
While Home Credit is currently using various statistical and machine learning methods to make these predictions, they’re challenging Kagglers to help them unlock the full potential of their data.
[feature engineering]
- collect many ideas from kernel
- interaction features
- indicator features
[modeling]
- many light gbm models with different dataset
- bayesian grid search
- cv predict
- boosting samples
- blend of base models
