東京大学グローバル消費インテリジェンス寄付講座

Homecredit dataset

Obejctive project are create a system to help loan assessments automatically & Business Metrics are daily resolved applications and average resolved time

kaggle data-mining competition

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 underser…

Build a machine learning model that can automatically assess loans with goal to predict client’s repayment abilities and speed up inspection filing without spending more money.

House Prices Prediction and Credit Default Risk Prediction competitions. Advanced decision tree-based regression and classification models are used.

My submission for the Home Credit Default Risk Kaggle competition. The objective is to predict how capable each applicant is of repaying a loan.

Reduce the rejection of creditworthy clients by accurately assessing clients' repayment abilities to ensure the creditworthy clients are approved and provided with suitable loan terms.