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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

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This project predicts the chance of loan default using Home Credit data, looking at factors like income and credit history...

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

This is a final project on virtual internship experience by Home Credit x Rakamin

kaggle data-mining competition

Explore Home Credit's default risk data using EDA and a Baseline Machine Learning models. Help predict future payment problems for clients, ensuring a positive borrowing experience and empowering financial inclusion

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.

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.

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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.

Complete implementation of Home Credit Default Risk Kaggle competition. Predicts credit default using Python, scikit-learn, SHAP, and ensemble models. Includes data preprocessing, feature engineering, and submission pipeline.