Kaggle Featured - Home Credit Default Risk

This is a kaggle competition investigating how capable each applicant is of repaying a loan.

1.final result

2.brief discreption

Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders. 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. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

3.dataset

Data in this competition tend to be very heterogeneous, collected over different time frames, and coming from many different sources that may change and alter in midst of the data collection process.

4.workflow

5.environment

• python3.5 and Anaconda2
• GPU: Nvidia Titan Xp
• OS: Ubuntu 16.04

6.code

I post some selected codes here