ETL process which downloads, transforms, and loads Freddie Mac/Fannie Mae mortgage data
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Updated
Dec 13, 2017 - Python
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.
A React component that enables users to calculate their mortgage payments, features data visualization.
An app under development and open for other parties to contribute.
End-to-end credit risk modeling system using Fannie Mae data, including training pipelines, persisted model artifacts, and a Streamlit-based loan scoring app.
Free online calculators for mortgages, loans, finance, savings, and everyday calculations.
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 risk analysis and forecasting project using Python and statistical modelling
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.
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