Data Analysis and prediction on Kaggle dataset: House Loan Data Analysis-Deep Learning
Use various techniques to train and evaluate a model based on loan risk. I’ll use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Webinar Resources from the BlueGranite Loan Risk Analysis Webinar: Banking - Loan Risk Analysis using Machine Learning - October 21, 2020
A smart loan risk analyser with machine learning and a user-friendly UI to classify applicants as Risky or Non-Risky.
A professional, modular Python system for loan risk prediction and analysis. Features include: dynamic risk segmentation, explainable AI (SHAP) insights, and interactive "what-if" scenario simulation via FastAPI endpoints.
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.
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