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.
End-to-end credit risk analysis of 32K loan applicants: Python cleaning, PostgreSQL analysis, Power BI dashboard.
Loan Default Predictor on Lending Club dataset
Binary classification for credit risk — predicting loan defaults with ML
Logistic Regression model predicting loan repayment vs default using financial attributes. Strong ROC-AUC (0.91) with business interpretability.
Production-ready Loan Default Prediction using LightGBM, Feature Engineering, Cross-Validation and Explainable AI (SHAP).
Interactive Power BI dashboard analyzing 255K+ loans to identify default risk drivers, financial exposure, and portfolio trends using Power Query and DAX.
EDA and hypothesis testing project to identify key factors in loan default analysis
Description: Credit risk analysis of 601 personal loans using Excel and SQL Server to identify default drivers and recommend underwriting thresholds.
SQL credit risk analysis project focused on default rates, loan grades, borrower profiles, and data quality checks.
SQL and Power BI project analyzing historical loan default patterns and rule-based risk prioritization.
Maturity-aware lifetime charge-off risk modeling with temporal validation, leakage-safe features, probability calibration, capacity-constrained manual review, FastAPI, and Streamlit.
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.
Analysis of Lending Club loan data (2007–2018) to predict loan default using a Logistic Regression model. Covers full EDA, visualisations of interest rate and income vs default status, and scikit-learn classification with class imbalance handling.
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