Built an ML-based loan default prediction system using borrower financial and credit data. Performed data cleaning, feature engineering, and model evaluation, achieving 87% training and 80% test accuracy. Used confusion matrix analysis to support credit risk assessment and underwriting decisions.

Project analyzes financial data from 1,000 customers to estimate credit risk and predict potential defaults. Using features like credit scores and transaction history, it aims to enhance decision-making in risk management.

Credit Risk Prediction project using machine learning and data analysis to identify potential loan defaulters and assist decision-making for financial institutions.

Machine learning‑based credit risk prediction system using borrower data to assess loan default probability. Features data preprocessing, Logistic Regression & Random Forest models, and an interactive Streamlit dashboard for real‑time credit scoring and risk analysis in financial services.

Risk Analysis in Banking is a data-driven project focused on identifying, assessing, and managing financial risks using statistical models and machine learning techniques. It includes tools for credit risk scoring, default prediction, stress testing, and portfolio risk evaluation to support decision-making in banking and financial institutions.

This project uses ML to predict the likelihood of credit default within two years using financial and demographic features. It includes data preprocessing, LightGBM model training, risk probability prediction, and exploratory data analysis to profile borrowers into low, medium, and high risk categories.

Credit Risk Prediction using machine learning to classify loan applicants as low or high risk. Includes data preprocessing, exploratory analysis, and model development with Logistic Regression, Random Forest, and XGBoost. Helps financial institutions minimize defaults and make informed lending decisions.

End-to-end loan default prediction project using exploratory data analysis, feature engineering, and advanced classification models. Implements Decision Trees, Random Forests, XGBoost, and CatBoost with rigorous evaluation and hyperparameter tuning to assess credit risk from real-world financial data.

Loan Default Prediction is a machine learning project aimed at predicting credit risk using financial data. It involves exploratory data analysis, handling class imbalance with SMOTE, and training models such as Logistic Regression, SVM, Random Forest, LightGBM, XGBoost, and ANN to achieve strong AUC and accuracy.