Current Page
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
Keep reading
Healthcare
Diabetic Retinopathy Blindness Detection
Healthcare
Depression Detector
E-Commerce
Recommendation Systems
Software Development