Overview
Built an AI-driven residential real estate prediction system designed to estimate home sale prices and sale timelines using large-scale county-level property data.
Problem
Residential market decisions depend on pricing, timing, property characteristics, and historical patterns, but the underlying data is high-dimensional and difficult to interpret without structured modeling.
Dataset / Inputs
- County-level real estate dataset with 200+ fields
- Property characteristics, valuation history, sale history, and date-based indicators
- Structured records designed for price and sale-timing analysis
System Architecture
- Cleaned and transformed county-level real estate datasets with 200+ features.
- Engineered variables from property characteristics, sales history, valuation data, and date-based fields.
- Helped design the analytical pipeline for model training and insight generation.
- Supported documentation and system design deliverables for the client-facing solution.
What I Built
- Data preparation workflow for high-dimensional property records
- Feature engineering layer spanning property, valuation, and time-based signals
- Analytical pipeline design for model training and downstream insight generation
- Documentation support for the broader client-facing capstone system
Tools
- Python
- Pandas
- Scikit-learn
- feature engineering
- predictive modeling
- system design
Results / Proof Points
- Structured 200+ property fields into a workable analytics pipeline for both price and sale-timing prediction.
- Built the foundation for a client-facing capstone system focused on valuation transparency and market timing insight.
Business Value
The project aims to improve transparency around residential property valuation and help users make more informed real estate decisions.
What I Learned
This project strengthened my ability to work with messy real estate data, translate domain fields into useful model features, and frame predictive analytics around a real client use case.
Next Steps
The next iteration will deepen model evaluation, sharpen business-facing outputs, and improve how pricing and sale-timing insights are communicated to users.
Limitations
The public case file reflects an active capstone build, so the current emphasis is on data preparation, feature design, and system framing rather than final production metrics.