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

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

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

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

Loan Default Predictor on Lending Club dataset

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.

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.

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.

End-to-end MLOps pipeline for loan default prediction — 4 models tracked with MLflow, GradientBoosting champion at AUC 0.868 / PR-AUC 0.397 on 7% imbalanced data, alias-based model registry, and a FastAPI REST endpoint with Pydantic validation.

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.

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

Credit risk assessment using FICO score segmentation, loan default modeling, discretization techniques, and log-likelihood evaluation for predictive analytics in financial services.

Explainable ML system for loan default prediction integrating cybersecurity-inspired behavioral features. 99.43% ROC-AUC. Master's thesis project.

Loan default risk prediction on 404K records using XGBoost + SMOTE. Weighted F1 0.988, CV F1 0.88. SHAP explainability, MLflow experiment tracking, Streamlit deployment. Python · scikit-learn · XGBoost