ETL process which downloads, transforms, and loads Freddie Mac/Fannie Mae mortgage data

Resources for Open Risk Academy Course: "Processing US Agency Mortgage Data with Awk and Pandas - Part 1: Static Data"

Resources for Open Risk Academy Course: "Processing US Agency Mortgage Data with Awk and Pandas - Part 2: Performing Book"

Fairness Analysis in US Mortgage Lending with Machine Learning Algorithms

This is a capstone project for Microsoft Professional Programme in Data Science

Mortgage Risk & Retention Analytics Platform: predicts loan fallout and refinance risk, supports portfolio segmentation, and optimizes underwriter capacity with executive-ready reporting.

End-to-end credit risk modeling system using Fannie Mae data, including training pipelines, persisted model artifacts, and a Streamlit-based loan scoring app.

An app under development and open for other parties to contribute.

A React component that enables users to calculate their mortgage payments, features data visualization.

In this study, we examine a sample of mortgage lending decision data from Boston in 1990 to determine whether race is associated with the outcome of a mortgage loan application.

Scalable ETL pipeline and Machine Learning model to predict mortgage defaults using Freddie Mac’s Single-Family Loan-Level Dataset. Migrated from a Pandas-based legacy system to a distributed PySpark architecture on Databricks to handle multi-gigabyte time-series performance data.

mortgage calculator