home-credit-default-risk

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

Deep Learning based Credit Risk Prediction using TensorFlow, Keras, SMOTE, and Feature Engineering.

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…

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

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.

End-to-end credit default prediction: SQL feature layer (DuckDB), XGBoost, SHAP, risk bands from unit economics, FastAPI + interactive UI

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

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

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.

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.

Credit-risk prediction notebook using classical ML baselines, LightGBM, and optional D-Wave quantum feature selection on the Home Credit dataset.

End-to-end credit default prediction with explainability (SHAP)