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LTV Prediction for Financial Institutions
Data Analysis and ML Development: Jupyter Notebook, Google Colab, Visual Studio Code
Libraries: Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn, PyCaret
API Development and Deployment: FastAPI, Uvicorn, Codecs
This project develops a predictive tool for assessing loan-to-value (LTV) ratios, a critical financial metric used by banks to evaluate lending risks associated with mortgages. With high LTV ratios typically indicating higher-risk loans, the project aimed to create a sophisticated model that can accurately compute LTV ratios based on behavioral machine learning techniques, thereby aiding financial institutions in managing credit risk effectively
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