End-to-end analysis of bank loan default risk using historical lending data to identify key risk factors, assess borrower behavior, and support data-driven credit decisions.

Uni-variate and Bi-variate analysis to understand the driving factor behind loan default

Loan Default Predictor on Lending Club dataset

Distribution-shift-aware loan default prediction — adversarial validation revealed 91.5% train/test separation, guiding a LightGBM/CatBoost/XGBoost ensemble across 50+ Modal cloud experiments. Deep Learning IndabaX Zimbabwe 2026. Public LB 0.6840.

Logistic Regression model predicting loan repayment vs default using financial attributes. Strong ROC-AUC (0.91) with business interpretability.

Predicting loan defaults using machine learning and hybrid feature engineering approaches.

EDA and hypothesis testing project to identify key factors in loan default analysis

Production-ready machine learning pipeline for loan repayment prediction using CatBoost with cross-validation and model evaluation.

Análise exploratória de risco de crédito utilizando dados de empréstimos, com foco em inadimplência (default). O projeto investiga como variáveis financeiras como score de crédito, renda e Debt-to-Income Ratio influenciam a probabilidade de default, reproduzindo análises utilizadas por instituições financeiras.

Machine learning project for predicting loan default risk using borrower data, helping financial institutions make data-driven lending decisions.

End-to-end loan default risk analysis project using Python, SQL, Power BI, and Machine Learning to identify high-risk borrowers, predict default probability, and support credit-risk decision-making.

Two-model ML pipeline predicting loan defaults & loss severity using Random Forest + XGBoost in R | MAE: 5.2261 | Recall: 60.95%

A machine learning–based credit risk prediction system using XGBoost, deployed as an interactive Streamlit web application to classify applicants as Good or Bad credit risk.

A machine learning project to predict credit risk (GOOD or BAD) for loan applicants using historical loan data from 2007–2014. This solution helps multifinance companies minimize default risk and streamline loan approvals through accurate risk classification and a modern graphical user interface (GUI).

Production-ready ML pipeline for retail banking default prediction with feature engineering, CatBoost models, and Dockerized FastAPI deployment on Google Cloud.

BankPrediction is a machine learning project that predicts customer churn, loan defaults, and detects anomalies using XGBoost models. It includes visual data analysis and a future prediction model for 2033.

ML system that predicts SME loan default risk using alternative data like GST filing rate, UPI transactions & cash flow patterns with SHAP explainability

XGBoost binary classifier with SHAP explainability to predict SBA loan default risk — 15+ engineered features, F1-optimized threshold, and AUCPR evaluation

Predictive Modeling for Loan Default Risk using Machine Learning