Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

machine-learning scikit-learn xgboost lightgbm ensemble-learning predictive-modeling risk-assessment loan-default-prediction model-deployment python-project flask-webapp model-interpretability real-time-prediction loan-risk-analysis loan-approval-system banking-analytics financial-predictions credit-risk-modeling web-based-ml pickle-model

• Updated May 24, 2025
• Python

📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.

machine-learning xgboost ensemble-learning predictive-modeling risk-assessment credit-scoring loan-data loan-default-prediction model-deployment loan-prediction-analysis flask-webapp risk-score loan-approval-prediction banking-analytics financial-predictions credit-risk-modeling web-based-ml

• Updated Feb 24, 2026
• Python