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 2: Performing Book"

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

Fairness Analysis in US Mortgage Lending with Machine Learning Algorithms

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

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

Analysis of mortgage default risk using Fannie Mae data, comparing loan characteristics and default patterns between 2007 and 2019.

Mortgage risk analysis and forecasting project using Python and statistical modelling

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

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.

mortgage calculator

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.

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.

A comprehensive mortgage payoff analyzer that simulates loan repayment scenarios with annual extra payments, generating detailed amortization schedules, PDF reports, and burndown charts to visualize interest savings.