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.
Predicting loan defaults using machine learning and hybrid feature engineering approaches.
Uni-variate and Bi-variate analysis to understand the driving factor behind loan default
Predicting loan default risk using Logistic Regression and CatBoost with business cost-based threshold optimization. Minimizes total financial loss by tuning decision thresholds using a cost-benefit matrix. Built with Python, CatBoost & Scikit-learn.
Loan-portfolio default analysis on 400 messy bank records: pandas cleaning pipeline (10 stages, 51 unit tests), feature engineering, scikit-learn logistic regression (AUC 0.617), risk-tier segmentation, and a Power BI dashboard spec with DAX measures and a 3-page layout.
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
Production-ready Loan Default Prediction using LightGBM, Feature Engineering, Cross-Validation and Explainable AI (SHAP).
SQL credit risk analysis project focused on default rates, loan grades, borrower profiles, and data quality checks.
Loan Default Predictor on Lending Club dataset
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.
Two-model ML pipeline predicting loan defaults & loss severity using Random Forest + XGBoost in R | MAE: 5.2261 | Recall: 60.95%
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.
ML-powered system to predict loan defaults and suggest recovery strategies
End-to-end Loan Default Prediction & Risk Scoring System using XGBoost and SHAP
Interactive Power BI dashboard for loan default risk analysis, credit risk assessment, borrower segmentation, and financial analytics.
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).
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